{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"[参考链接 - facebook/prophet/notebooks/quick_start.ipynb ](https://github.com/facebook/prophet/blob/master/notebooks/quick_start.ipynb)","metadata":{}},{"cell_type":"markdown","source":"## [1. Prophet](https://github.com/facebook/prophet)\n","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nfrom fbprophet import Prophet","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:00:51.661697Z","iopub.execute_input":"2021-09-03T08:00:51.662214Z","iopub.status.idle":"2021-09-03T08:00:51.668291Z","shell.execute_reply.started":"2021-09-03T08:00:51.662175Z","shell.execute_reply":"2021-09-03T08:00:51.666274Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv('http://mirror.coggle.club/prophet/example_wp_log_peyton_manning.csv')\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:00:51.678275Z","iopub.execute_input":"2021-09-03T08:00:51.678865Z","iopub.status.idle":"2021-09-03T08:00:53.470530Z","shell.execute_reply.started":"2021-09-03T08:00:51.678825Z","shell.execute_reply":"2021-09-03T08:00:53.468858Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"           ds         y\n0  2007-12-10  9.590761\n1  2007-12-11  8.519590\n2  2007-12-12  8.183677\n3  2007-12-13  8.072467\n4  2007-12-14  7.893572","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ds</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2007-12-10</td>\n      <td>9.590761</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2007-12-11</td>\n      <td>8.519590</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2007-12-12</td>\n      <td>8.183677</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2007-12-13</td>\n      <td>8.072467</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2007-12-14</td>\n      <td>7.893572</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"m = Prophet()\nm.fit(df)","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:00:53.472319Z","iopub.execute_input":"2021-09-03T08:00:53.473095Z","iopub.status.idle":"2021-09-03T08:00:55.727620Z","shell.execute_reply.started":"2021-09-03T08:00:53.473028Z","shell.execute_reply":"2021-09-03T08:00:55.725974Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"<fbprophet.forecaster.Prophet at 0x7f3fb38f0810>"},"metadata":{}}]},{"cell_type":"code","source":"future = m.make_future_dataframe(periods=365)\nprint(future.shape)\nfuture.tail()","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:00:55.729629Z","iopub.execute_input":"2021-09-03T08:00:55.730054Z","iopub.status.idle":"2021-09-03T08:00:55.745081Z","shell.execute_reply.started":"2021-09-03T08:00:55.730017Z","shell.execute_reply":"2021-09-03T08:00:55.743856Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"(3270, 1)\n","output_type":"stream"},{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"             ds\n3265 2017-01-15\n3266 2017-01-16\n3267 2017-01-17\n3268 2017-01-18\n3269 2017-01-19","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ds</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3265</th>\n      <td>2017-01-15</td>\n    </tr>\n    <tr>\n      <th>3266</th>\n      <td>2017-01-16</td>\n    </tr>\n    <tr>\n      <th>3267</th>\n      <td>2017-01-17</td>\n    </tr>\n    <tr>\n      <th>3268</th>\n      <td>2017-01-18</td>\n    </tr>\n    <tr>\n      <th>3269</th>\n      <td>2017-01-19</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"The `predict` method will assign each row in `future` a predicted value which it names `yhat`. If you pass in historical dates, it will provide an in-sample fit. The `forecast` object here is a new dataframe that includes a column `yhat` with the forecast, as well as columns for components and uncertainty intervals.","metadata":{}},{"cell_type":"code","source":"forecast = m.predict(future)\nforecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:00:55.746802Z","iopub.execute_input":"2021-09-03T08:00:55.747132Z","iopub.status.idle":"2021-09-03T08:01:02.304985Z","shell.execute_reply.started":"2021-09-03T08:00:55.747103Z","shell.execute_reply":"2021-09-03T08:01:02.303620Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"             ds      yhat  yhat_lower  yhat_upper\n3265 2017-01-15  8.213670    7.485685    8.929720\n3266 2017-01-16  8.538673    7.841039    9.253344\n3267 2017-01-17  8.326104    7.588119    9.025543\n3268 2017-01-18  8.158756    7.437556    8.888372\n3269 2017-01-19  8.170689    7.439750    8.910358","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ds</th>\n      <th>yhat</th>\n      <th>yhat_lower</th>\n      <th>yhat_upper</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3265</th>\n      <td>2017-01-15</td>\n      <td>8.213670</td>\n      <td>7.485685</td>\n      <td>8.929720</td>\n    </tr>\n    <tr>\n      <th>3266</th>\n      <td>2017-01-16</td>\n      <td>8.538673</td>\n      <td>7.841039</td>\n      <td>9.253344</td>\n    </tr>\n    <tr>\n      <th>3267</th>\n      <td>2017-01-17</td>\n      <td>8.326104</td>\n      <td>7.588119</td>\n      <td>9.025543</td>\n    </tr>\n    <tr>\n      <th>3268</th>\n      <td>2017-01-18</td>\n      <td>8.158756</td>\n      <td>7.437556</td>\n      <td>8.888372</td>\n    </tr>\n    <tr>\n      <th>3269</th>\n      <td>2017-01-19</td>\n      <td>8.170689</td>\n      <td>7.439750</td>\n      <td>8.910358</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":" # plot the forecast by calling the Prophet.plot method and passing in your forecast dataframe.\n%pylab inline\nfig1 = m.plot(forecast)","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:02.306350Z","iopub.execute_input":"2021-09-03T08:01:02.306677Z","iopub.status.idle":"2021-09-03T08:01:02.897993Z","shell.execute_reply.started":"2021-09-03T08:01:02.306647Z","shell.execute_reply":"2021-09-03T08:01:02.896062Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"Populating the interactive namespace from numpy and matplotlib\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['clf']\n`%matplotlib` prevents importing * from pylab and numpy\n  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x432 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAsgAAAGoCAYAAABbtxOxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydd5xU1fmHnzuNLlVUREGl7rLL9gYiiiUag0FU7BoLMcYkxsT+M7FjiYpdsaOIIEWxYENQyixbKUvTGCu9LGyfueX8/rhz78zszvY2654nH8POnZl77j1z77nvec/3fV9FCCGQSCQSiUQikUgkADja+wAkEolEIpFIJJJoQhrIEolEIpFIJBJJCNJAlkgkEolEIpFIQpAGskQikUgkEolEEoI0kCUSiUQikUgkkhBc7X0ADWHAgAEMHTq0vQ8DVVVxu93tfRhRgewLE9kPJrIfTGQ/BJF9YSL7wUT2QxDZFybR0g8//PAD+/btq7G9QxjIQ4cOJS8vr70Pgx07djBo0KD2PoyoQPaFiewHE9kPJrIfgsi+MJH9YCL7IYjsC5No6YeUlJSI26XEQiKRSCQSiUQiCUEayBKJRCKRSCQSSQjSQJZIJBKJRCKRSEKQBrJEIpFIJBKJRBKCNJAlEolEIpFIJJIQWs1Avuqqqxg4cCBjxoyxt911113Ex8eTkJDA6aefzo4dO1qreYlEIpFIJBKJpEm0moF85ZVX8sknn4Rtu/nmm9mwYQPr1q3j7LPP5t57722t5iUSiUQikUgkkibRagbyhAkT6NevX9i2ww47zP67vLwcRVFaq3mJRCKRSCQSiaRJtHmhkDvvvJPZs2fTu3dvli9f3tbNSyQSiUQikUgkddLmBvIDDzzAAw88wIwZM3jmmWe45557In5u1qxZzJo1C4Bdu3ZFhV5579697X0IUYPsCxPZDyayH0xkPwSRfWEi+8FE9kMQ2Rcm0d4P7VZq+pJLLuGss86q1UCePn0606dPB8wygNFQjhCImuOIBmRfmMh+MJH9YCL7IYjsCxPZDyayH4LIvjCJ5n5o0zRv3377rf33+++/z6hRo9qyeYlEIpFIJBKJpF5azYN80UUXsWLFCvbt28fgwYO55557+Pjjj9m2bRsOh4MhQ4bwwgsvtFbzEolEIpFIJBJJk2g1A3nu3Lk1tl199dWt1ZxEIpFIJBKJRNIiyEp6EolEIpG0IV6vlxkzZuD1etv7UCQSSS20W5CeRCKRSCSdDa/Xy6RJk/D7/Xg8HpYtW0ZmZmZ7H5ZEIqmG9CBLJBKJRNJGrFixAr/fj67r+P1+VqxY0d6HJJFIIiANZIlEIpFI2oiJEyfi8XhwOp14PB4mTpzY3ockkUgiICUWEolEIpG0EZmZmSxbtowVK1YwceJEKa+QSKIUaSBLJBKJRNKGZGZmSsNYIolypMRCIpFIJBKJRCIJQRrIEolEIpFIJBJJCNJAlkgkklZG5r2VSCSSjoXUIEskEkkrIvPeSiQSScdDepAlEomkFZF5byUSiaTjIQ1kiUQiaUVk3luJRCLpeEiJhUQikbQiMu+tRCKRdDykgSyRSCStjMx7K5FIJB0LKbGQSCQSiUQikUhCkAayRCKRSCQSiUQSgjSQJRKJRCKRSCSSEKSBLJFIJBKJRCKRhCANZIlEIpFIJBKJJARpIEskEolEIpFIJCFIA1kikUgkEolEIglBGsgSiUQikUgkEkkI0kCWSCQSiUQikUhCkAayRCKRSCQSiUQSgjSQJRKJRCKRSCSSEKSBLJFIJBKJRCKRhCANZIlEIpFIJBKJJARpIEskEolEIpFIJCFIA1kikUgkEolEIglBGsgSiUQikUgkEkkI0kCWSCQSiUQikUhCkAayRCKRSCQSSQvi9XqZMWMGXq+3vQ9F0kRc7X0AEolEIpFIJL8WvF4vkyZNwu/34/F4WLZsGZmZme19WJJGIj3IEolEIpFIJC3EihUr8Pv96LqO3+9nxYoV7X1IkiYgDeRmIpdRJBKJRCKRWEycOBGPx4PT6cTj8TBx4sT2PiRJE5ASi2Ygl1EkEolEIpGEkpmZybJly1ixYgUTJ06UdkEHRRrIzSDSMoq8ESQSiUQi6dxkZmZKe6CD02oSi6uuuoqBAwcyZswYe9vNN9/MqFGjiI+PZ8qUKRw8eLC1mm8T5DKKRCKRSCQSya+PVjOQr7zySj755JOwbaeddhpFRUVs2LCBESNGMGPGjNZqvk2wllHuu+8+Ka+QSCQSiUQi+ZXQahKLCRMm8MMPP4RtO/300+2/MzIyWLBgQWs132bIZRSJRCKRNASv1yt1qRJJB6HdNMivvvoq06ZNq/X9WbNmMWvWLAB27drFjh072urQamXv3r3tfQhRg+wLE9kPJrIfTGQ/BJF9YWL1Q15eHtOmTUNVVdxuN/PmzSMlJaWdj67tkNdDENkXJtHeD+1iID/wwAO4XC4uueSSWj8zffp0pk+fDkBKSgqDBg1qq8Ork2g5jmhA9oWJ7AcT2Q8msh+CyL4wGTRoEJs2bUJVVXRdB2DTpk1Mnjy5nY+sbZHXQxDZFybR3A9tbiC//vrrfPjhhyxbtgxFUdq6eYlEIpFI2hwrqNtKCyqDuiWS6KZNDeRPPvmERx55hK+++oru3bu3ZdMSiUQikbQbMjeuRNKxaDUD+aKLLmLFihXs27ePwYMHc8899zBjxgx8Ph+nnXYaYAbqvfDCC611CBKJRCKRRA0yqFsi6Ti0moE8d+7cGtuuvvrq1mpOIpFIJBKJRCJpEVotD7JEIpFIJBKJRNIRkQayRCKRSCQSiUQSgjSQJRKJRCKRSCSSEKSBLJFIJBJJG+L1epkxYwZer7e9D0UikdRCu1XSk0gkEomks+H1epk0aZKdD3nZsmUys4VEEoVID7JEIpFIJG3EihUr8Pv96LqO3+9nxYoV7X1IEokkAtJAlkgkEomkjbAq6jmdTllRTyKJYqTEQiKRSCSSNkJW1JNIOgbSQJZIJJJ2wOv1SiOpkyIr6kkk0Y80kCUSiaSNkYFaEolEEt1IDbJEIpG0MdEUqCVTjkkkEklNpAdZIpFI2hgrUMvyILdXoJb0ZEskEklkpAdZIpFIWoG6PLNWoNZ9993XrkZpNHmyJRKJJJqQHmSJRCJpYRrimY2GQK1o8WRLJBJJtCE9yBKJRNLCdBTPbLR4siWSXxtS29/xkR5kiUQiaWE6kme2NT3ZMpVdENkXnQep7f91IA1kiaSFkA9AiYUsBgF5eXlceOGF0khAGkydjUgrSPL37nhIA1kiaQHkA1BSnWjQGLcnXq9XGgkBpMHUuehIK0iS2pEaZImkBegomlMLqY+TtDaZmZl4PB6cTmenNxIsg0n2RedAavt/HUgPskTSAnQkj4H0dkvagpSUlE4vM7GQkpvOR2dfQfo1IA1kiaQF6EgPQLncK2krpJEQRPaFRNKxkAayRNJCdJQHYEfydv/akYGdEolEEp1IA1ki6WR0JG/3rxkpdZFIJJLoRRrIEkknpKN4u3/NSKmLRCKRRC8yi4VEIpG0AzKzQedBZo2RSDoe0oMskUgk7YCUunQOfm0FU6RuXtJZkAayRCKRtBNS6vLr59dUMEXq5iWdCSmxkEgkEomklfg1FUzpaAWRJJLmID3IEolEIpG0Er+mgikyRaSkMyENZImkFZA6PYlEYvFrkdJI3bykMyENZImkhZE6PUljkRMqSUfh12LsSyT1IQ1kiaSFkfltJY1BTqgkEokk+pBBehJJCyPz20oagwx8kkgkkuhDepAlkhZG6vQkjUEGPkkkEkn0IQ1kiaQVkDo9SSh1aYwzMzOZOXMmCxcuZOrUqfK6kUgkkihAGsgSiUTSitSnMfZ6vdx44434/X5WrlxJXFycNJKjEBlIKZF0LlpNg3zVVVcxcOBAxowZY2979913iY2NxeFwkJeX11pNSyQSSdRQn8ZYapCjH2uSc9dddzFp0iS8Xm97H5JEImllWs1AvvLKK/nkk0/Cto0ZM4ZFixYxYcKE1mpWImkXvF4vM2bMkA9OSQ3qC9qUQZ3Rj5zESCSdj1aTWEyYMIEffvghbNvo0aNbqzmJpN2QabokdVFf0KYM6ox+ZCClRNL5kBpkiaSZyLzHkvqoL2hTBnVGN3ISI5F0PqLWQJ41axazZs0CYNeuXezYsaOdjwj27t3b3ocQNci+MNm7dy+xsbG43W4A3G43sbGxUXG9tiXyejCR/RDk19YXQ4YM4YorrgBo1P39a+uHpiL7IYjsC5No74eoNZCnT5/O9OnTAUhJSWHQoEHtfEQm0XIc0YDsC5OxY8fy5ZdfdnrvkrweTCL1Q2fNgCCvCRPZDyayH4LIvjCJ5n6IWgM52umsDzxJZOQSuaQ2pEZdIpFIOh6tlsXioosuIjMzk23btjF48GBeeeUVFi9ezODBg/F6vfz2t7/ljDPOaK3mWxWZ8kciqYnM5BEZmQGhYyCvX4lEEkqreZDnzp0bcfuUKVNaq8k2QwZlSSTh5OXlceGFF0ovaQRkBoTop6lefrmSKJH8epESiyYgH3gSSTher1dOGmshIyODzz7/gpVff9XhDKnOYgA2xekhpTMSya8baSA3AZnyRyIJJzMzU04aa2FHSRXdjo3h9tuz2vtQGkVnMgCrOz369+/PjBkz6hzf5UqiRPLrRhrITUQGZUkkQVJSUuSkMQIVfo0yn44hRHsfSqPpTAZgqNOjf//+3HjjjfVODORKokTy60YayBKJpEWQk8aabN5dioJCfeZxNEoZOpsBaF2/M2bMaNDEoDkridH4e0skknCkgSyRSCSthCHAqUBdDuRolTJ0VilZYyYGTZkURuvvLZFIwpEGskQikbQSQgD1GMjRLGXojKsCrT0xiObfWyKRBJEGskQikbQSlmEs6hBZdDYpQ0egNScGHeH3lhIQiUQayBKJRNIqFO0sCQTnKbV6kC1DZObMmezfv18aJL9Sqhuc0SxdaU0JiDS8JR0JaSBLJBJJK1Cl6YDpPY7kQZZa1OilJQ252oroROtv3VoSEFlMSNLRaLVS050JWaJUIpFUxxAgMGUWkTzIsgR1dGJNXO666y4mTZrU7HE9UhGdaMaSgDidzhaVgHS0fpBIpAe5mUgvkEQiiYRpFAtELWneOoIWtTPS0h7UjlZEp7UkIB2tHyQSaSA3ExmRLJFI6kIIIhYKiTYtqtSHmrT0xKUjFtFpDQlIR+wHSedGGsjNRHqBJBJJdbxeL7MXf8zYtHEkpqZTWxKLaNGiypWwIK0xcYmW37m9kf0g6UhIA7mZRJsXSNJySI+apClYxqbP58ftcfPUnPcYEZ/MwUqVPt3c7X14EWmtlbCOeg+1liHXUftDIumMSAO5BZCz4l8f0qMmaSqWsWkYOqoKBd5VDItL4r/7ykg5pm97H15EWmMlTGYtCEeOKRJJx0JmsZBIIiAzDEiaSub4E3G5PTicTtxuN4kZ4+xsFtGKtRJ23333tZjhJrMWhCPHFImkYyE9yBJJBKS2XNJUklMzePbtxaxdvYqE9CxiElPxawZGFBvI0PIrYTJrQThyTJFIOhbSQJZIItBcbbnUGnZu4pLTGBafDJgZLKLdg9wayKwF4ch4FYmkYyENZImkFprqUZNaQ4kQ2JkrdCHMYiG1pbLoADR1wifjM8Kx+sMqLiUNZYkkepEGskTSwsjc2J0bRQnmPnYoCj7NwOlwmJX1hEBRlPY+xEbR2SZ8rb3609n6UyLpqMggPYmkhWmtUq2SjoMhBAamE1nVBUJY/7X3kTWezhRc1pQy05Y3uKElqTtTf0okHRnpQZZIWhipNZRYmmOBMDXIotZaIVFPZwoua+zqT1O8wf3798fhcCCE+NX3p0TSkZEGskTSCkjtZedFwZRSGAKcCgFPsrCD9ToanWnC19jJQFMM6htvvBFd13E4HMycOfNX3Z8SSUdGGsgSiUTSgljxeUIIdKHw2btvkbPsI045azLjbrsR04Q26SjZTjrLhC8zM5OZM2eycOFCpk6dWu85N9WgNgwDRVHYv39/Cx69pKPRUe7/zoo0kCUSiaSFUHVBhV83NchC8PHc2bx43y0AFK75ihP69+BP1/0RkMFa0Yjl4fX7/axcuZK4uLg6f5PGetdPOumkTiNXkdSNvP+jHxmkJ+nUNDbARiKpi18OVfLTwQpbc7x0wZyw9xctWmT/vWLFCnw+H7qu4/P5ftXBWt/tK6fgl4Pk/3ywvQ+lTpoSQJeZmcntt9/eIOPmqJEJPPv2e0y/6Q5pENG5x18ZrBn9SA+ypNPS0jP4X/ty2a/9/FoKQ0BRQQ4fL5zHd1s2hr03Zcq59t/9+/fHMAzzO4ZB//792/Q425LiSj8Q/cVSWjsgsULVGZOUSkxiKulD+rbovjsand2D2pmCXzsq0kCWdFpaMl9x6GDvdDq56qqruPzyyzvsgF/dGP61P8xayvjftK6QT5Yu5cMFc9BUFRFiEY479UyuvvYa+/X+/ftxOBwYhoHD4fhV61Gtbohy+ziiZKIlJ4YKZh+sz8/hy7cLOvVks7Pni+9Mwa8dFWkgSzotLTmDDx3sdV3nxRdf5I033uiQhmQkY/jX/DBrKePf6/Xy12svx+/zhRnGAE6ni5QTJ4VtmzhxIl26dOkUHiSrN6LdgwzhAYmtMTHckJfDXy+Zgqb+OiebDUV6UDtP8GtHRWqQJZ0WawZ/3333NfshZQ32VpU0IUSH1ZVFMoYbW/xk065S9pf72+aAm0lLaQFXrFiB6vcHjeORJ8JNS+D6OehHx/LCjP8L01paGROSk5M544wzWuBMog9LY7oxP9euLtiRaA2daH72alSpPW3R8VciaQ2kB1nS6ai+ZNoSA3NGRgaz5i1h0Ttv8+GCtzF0rcN6RSJ5dhq7HFipapRUOenfw9NGR910avNk1be0rukGAnA7HfZ+3B4PqqridDrpe8rF7AHo2gtOvR7fa9cx5803mXjieHv/f/nLX/D7zYnE0qVLWb58+a/GUMjLy+PCCy/E5/fjdnt4es5iYhNT2/uwGkVLezkVBRLTx+H2uNFUOuwY0VJID6okmpEGsqTReL1elixZwuTJkzvc4NZaWlrdEIxKSOaWuGTOPn8a+d7VXPr7Mztc/0DL6DANAQcq/HhcCkf37tYGR910ElLS+HDpp6xds6pRmuste8owhGDsoN6A2W9PvzybvHUbGJWUwavfGuw5FPIFIXjj9de46g9XkpmZaXqcVdV+u72lK1Wqjk8z6N3N3SL783q9Zs5fXUfDT0H2KmISU1pk322Bdc3PnDmT/fv3N0knGjpWJqemIwTEJqXy+OzFHPpvodSeSiRRjDSQm0BnjuYPNRyefPLJDrc01ppaWkOALgTxyWnEJ6eRckzHjVJvrg5TCECBnSVVUW8gb9tTxmHHj+H2k060t9V3nVjpyqpLBuITkhiRcTIlVRolOZ+AI5CZQjG9zJqm2fuaOHEiTqcTTdMAU5bTnpksfiyupNSntsh16/V6WbduHQAOhwOX20Ni+ngMYfZd4tG9cTiUunfSjrTERLr6WPns2+8Rn5zGxvwc1q1dxRmnTiItPaOVzkAikTQXqUFuJNagd9dddzFp0qROl7+xo+dubKyWtjFYGstFc17nhkunMmvWrBbbd3vS2N/cMIQdjGV0EMlpdWlsfdeJWTa6ZulogZkA2a8LeveraeyG7iszM5Ozzz477P3CwsJmnknzaInfy+v1MnHiRD755BN0XQdF4cZ/PYAA3nxuJuvz10Z9NouWGOeq7yPPu4oN+TnceNkUXnniIS6Z8luWf72q5Q9eIpG0CNKD3Eh+zdH8DaGjRx63Vmods7QwvD/3DR77v38AsPbr5QBMnz69RdpoLxr7mxduPxQwHZWozlrg9Xr55PNlDI1PIzYpXBtb33ViCHAqkbMyCEzJjRLoAwBP1+5MuuBy/nH9NWH7OvLII1v6tJpESZWKXzda5PeqLh0RhsG2og3MvPcOVL/K7Gc9jPjiC8aPy2p+Y61ES4xz1feRmD6OPO8qVL+KYehoqp9VK7/m1Ikn1r8ziUTS5kgDuZF0dAOxuViGQ0fVIEPrBYYIBF998kHYtoULF3YoAzmSfKixkwqzzHJbHG3TsVaCrACyZ+YsJu3YYCYJn6YzJjG11nMVImgcbz9UydG9u/HN3jLKfDpOj2D+8jw2HAxKCLr17Mmf/vQwaScMCNtPYmJina/bim/2lqG00IRm4sSJuN1uO/jQ5TYDNUMNw69WrIhqA7klJtLVx0rHoFEoCrg9blTV7JcTJ0xohaNvPp1ZRiiRWEgDuZHI5N5mHwwZMoRBgwa196FEDaa8Ak4842zyVq2wt0+dOrX9DqqR1KW7bMykwjSOReDv6LSUrZWgYADZanyTT6WLywnA9/srKPNrpBzTF1U37EwVFoYQOAMGpaWzPlRpek2LCnNZ+Eu4vtbv92MIwTd7yxh+eA8O62oGwkVLsRAhzAwLhhCU+TR6dmn6o8EKQHz++eepws2kc84HFJYuesc2DCecdFLLHXwr0RIT6dCxMvuHYuKT03h89iLyvKvJHH8i6RnR9/z4tRcFkkgaSqsZyFdddRUffvghAwcOpKioCIADBw4wbdo0fvjhB4YOHcr8+fPp27fjBTKFDpwdIeBE0voIBEIIzjz/cro4HSxf+gFXXXphh/Iet5R8yJosWH9HI6ErQS63h8SMcRTtLCX5mD72Z6xj37CjhBGH96RX1+BwaeqPlcDvbt77hoBNGwq59U9/gOvnh7XncntsjXqVanBY1+BxuFwuVFXF5XK1y4qU1+vlzfeWkpo5nhFjU9i6p7TZgXqZmZkcfcyx7Da6k5ezlvVrV3HD/z3ItqL1uJ2dc6w0hMAREN6Y1017H1FkOruMUCKxaDUD+corr+SGG27g8ssvt7c99NBDTJo0idtuu42HHnqIhx56iIcffri1DqFVqL70JBBohsAjDeROy+5SH78crERgPgSPHxnDweJi4uLi2vvQGkVz5EN5eXls2rSJk046CWXQqJDSwtFpBVgrQW8uXkpy5jhiElPDvN2KYhoyG3aUYAiBHnjPMASF2w9hiKDMwvqWbgjW5+Wg+tUa7bncHvTAxEGpNlRYxWWU6m+0AbbUxOfnVY+HmW8uIjktvUX2rQAb8nP4+2VTUP0qzoB3Xtd0Plk0r9N5JgWC995+g4f/72YMXWfOs48x7LPPOeWk6NIgd3YZoURi0WoG8oQJE/jhhx/Ctr3//vt2NPAVV1zBxIkTO5SBbCW+twaO2Ys+5NiY9tEMSqKHSlVHIDCEYFNhLndcdT6q388bz/ynQxkBTZUPeb1epk2bhqqquN0eZr61mFFjk4Ho9SCDeb4cNRKnQ0HTa+qmhQC/roeZ+NYkyDKmreBMVTcQmCn+3B43vgjtGRGE2bNnz8YfqL4XmgKurbClJgFtcOHa1SSmtoyBDJDvXW1rj4VqFlYhpMpkR7k3WoKN+bm2cQyg+n28PeetqDOQpYxQIjFpUw3y7t27OeqoowAzenv37t21fnbWrFl2mqxdu3axY8eONjnGuvjiiy/Clp6WfvQh0w4/mp1KRadbNty7d297H0KzyMvLw+v1kpmZSUpK04sX7N27F61LFcUVKj5VJ/erz1D9PgzDwO/3s2TJEoYMGdKCR966DBkyhCuuuAKgwffckiVLAkaWgcCP98tPGDT4GDSPE59msKNrJHMxOji0rwSHoqALgVNR2OGuBOBAcSUlVRqOQKaK3XopFV1cGEJQvLcMv6bjcznRhYFDcfD1gT1UqQbHHjuEfz35MnduC29HGAblxXs5qHdhr9YNtcRNXl4er776arA0NeB0Ott0rIuNjcXtdiMAl8vN8FExHNy3x+4HC80QuGpZJavtXtq7bx/DR4/G5XajaeBwOFEA3dBxu93ExsZGxbheFy0xTlhjpffLT23j2KKivDwq+2DIkCHExsayZMkS9u7dS0pKSrP7oqM/M1oS2Rcm0d4P7RakpyhKnUuK06dPt/WbKSkpUREQduqpp/Lyyy/j9/txezxknXIGvQcM5MijDrODezoT0fCbNAWv1xu2EtBcL6/atQ9aqY8Kv0bi+NNY/MZLqKq578mTJ3fYfmookydPZubMmaaO1u0hbeIZ9Ox3OD26uPBoBoMGtV/xi/r43tcFl8OBZhg4FIVBg/oBUOoqgwo/jsAYdcTAnvTp5kbTDbZrh6jwa3T3uFB183tup4LTr+N2Ohg7/njYlhPWzkGfzgvZP5FwIIdzfnMqMZNOYtOmTWae4ACGYXD33Xdz4okntpnXbvLkyXz55Ze8sehj0saN54S4ZNwOh90P5nGZspLkQX1qfL+ue0nVBWkTz+A/byykKM/LqKQMBLCtIJuLJ5/BSYGS29FKS44TgwYNIv3k05n7ynOogeweTpeLq679Y1SOD9XPfebMmdx4443N7gvrXEurNA5U+Cn1aYw56rCWPvwOQTT+7u1BNPdDmxrIRxxxBDt37uSoo45i586dDBw4sC2bbzYpKSn20tPYtCz6D4uP2ih9Se20RhCKEGAAI8cm87e7HmDFJx9w9aUX/uqWJ2tLAzdv3jzWFqznuPg0hscnowsRlgotWgk9Puvvcp+GFih2YiBQCE7kbYkFQf2xuc0qHgI+3YjY1tp9kDtzBm88+xhfLltmaz2rqqoQwgzybA/pQWZmJsaRI/A4HVT4q0lKhEA1jFrHubrvJfOcRiWkkpCazv5yP26ng9T0DFJDgiGjjf3lfn4qrmzWOBF6n1grSLGJqTw7dwnvz5+LZhj87vyLojKLBdT8XRcuXNiiY+bech8HKvxRPz5IOjdtaiBPnjyZN954g9tuu4033niDc845py2bbxGsDBZ7y3x8t6+iQxgBknBaIwjFCBiEW9bn8eR9ZkGEjXnZxMXF/WqM5LrSP6WkpJBx8hl8f6CCCr9ZOtmqNhfNhAbboQjWbT+EZhhmTuBAhgolcA4HK1V+Kq40tcoiJBOBEiiKgmlQ/ndvWe3tGTpqwMC4/fbbWbZsGbNnz+a1115D07R2C4oKNfaFELz23mf8UpRLQsY4Bo1MiPgrer1efvrpJ5xO8zESueJgUKdtBPZtTSSilXK/hmYYTR4nqt8n77zzDpMnT0ZgGsmHD49HMwT9uruj9v6ofu5Tp05l5cqVzR4zrYnD8MR0hsQkRn2+dEnnptUM5IsuuogVK1awb98+Bg8ezD333MNtt93GBRdcwCuvvMKQIUOYP39+/TuKQkqrNEqqNCIXm5VEOy0dhKIoQS9iUa7XDkqK1kCkphYBaIhHzTKEoGOUmbbuXyt1sxrw/ip2lbxg2olDlSo+TTe9vaH7EMEJkgBu/WhrnW2GpnOzJtyXX355uwZFBdPyCTbk53HT5eeiqaaU7Jk5ixmTlIYQwpbFhRqBTqeTcy68jH/+eXrYsZsTDxH8zxAY1s0S5Qig93FjmjROVL9PvF6vaSALwcaCHFas+IqY5EyyxmVGrXMl0hgZFxfXrGs0NMjd7fbwzNuLiU0Mv64kkmii1QzkuXPnRty+bNmy1mqyzfh2X5ldLSw0zZOk49DS1fQsIyAmOQOny4lQDZxOZ9SlSGpOEYCGeNQsr6ppYEb/zSFCPMFBDypmcF7gM5YH2SykARsLcslds4r0cSeiGwbr1q4hJXM8x41JYvP6QqBnnW1OOu2MGn3eWtUdG4LX6+WthR+RlnUix8cl8enid/D7TNkHfj/53tUBQyaYoi7UCAQ4YtAxYcdf5tPYVerD8HQBQAtMKkTIv9GMEJCdvYbdWwoabRBWv09S083vbirM5Z9XTEX1qzicTn4z9SL+8serOT1KS01XvyZDXzdlku31ekOuGfO6Gp2QyrY9ZQzt152u7s4XxyOJbmQlvSZieZc6wmAvaX0swyr0UohGr0hzdJX1ed4tA9IQ4CRocEYzQc9vwJNcTZMsAhpk6x4Pzev71rOPAmZe39nPurnu9vt5/sH/gz/XvTI28IgjWudkmkBoHuTZzz7O9Xfex9KFc+3zVRwOkjLGIRAUV6r07urC5XTUKLSSlDEubL/7yv2U+zUwq0xTpRoYAnSsyWQbn2gDsIy+0UmZ7Cn38ddLpqCpjZ9Iht4nCWlZePodidfr5Y2nHzVT+hkGhqHz0bzZLHt/fodKBQlNn2RnZmba14w7UJxHICjzmyuy0kCWRBvSQG4iYYE5UTjYS1qH2jwnVkDXxjwvemAZvj3y2tZHqGHj9njIGNe4TAL1ezpNTzqKErg3ovvmECF/WAF4JortWTaA/+2vAKAge1VYXl8IBLKpsPKzD9FUf53tuT0epl18aWucSpMIzYPs91Wx9N230VTNfl8YBgbmWPf9gXKO7t2Now7rSmZmJvOXfMzHn31JatZ4RiXUTPtliIA8G6jSDESgiqDRNfoeO6FGn9vj4TdTpqFa/dKAieSOQ1UU5GaTs2YVZ542yb5PdpVUMX/Re9x2/VVUVfkQwrD1O+0VlNlcmjrJDg1yH5GYwaBRY9GMmvnHJZJoIfpGqg6C7S2UN3enIZLnxNP3CNADhR50QWJ6FotfdqOpkYOW2hu7gtx7S0nJHMeA4WNrfKapGmUIry5XWqXxw6YCFr6czSW/PzPqjADDCJdWWLIphYBH2dLQKoEywYpCQvp43B43qmrmLAbQdTOv7/jTfktRXjZ1mcgPvraQ1PSMNji7hjFx4kQcTie6bk7qvt28AcWhIALZ54QQFHhXEZuYYpfUtjhy5FguGxaPx+nAp9XM3GFfC1Y/EiiWEoXKmzDJiN+PwJzMWB5k6z7+dm8Zg/t0o4vTgSMkL/Qny7/i+oumoKp+/vPQg7ZXVVEU1uXl4PP7EcJAcTg4bnQ8P32zGcPQcUfhGFEfzQlytiYO3++vYGdJlXQwSaIaaSA3AbuMbiAiWw94A6JxSV3ScqxYsQKfzywC4vP5WLFiBalnnEvfHrCxIIc872pOPHEC97w0n/+tW8sfpv426oxCMB9SjkGjQgLRgjRk+TSSAW15igWmJ10IhaLCXO6+9gJUVeXVp6OrqqAQZm7fsECywHYzq5sSOBdwBGQjDgViE1N45PWFFK5dTWrmeHQhWL92DUkZ4zhuTBIDjzyS//um9nZHjk2JKq96ZmYmvzv/Yha//YbdD5knn072is8RhoHb04XEjHG2dCaU0G2RzshUoQWlK5Z33ordiCaqS0bOmDKNs869kL3b8jnl5JPt6/ZQlUrJLo0BPTwM6dfd/n6edzWq6seI4FWNT07D7fag4sflcnPZP+5GCMH3G3K46Hc19ejRTnODnL1eL8/MeoUKv85pv7+A5LTomTBKJKFIA7mR+DTdfihY+VC/3VvOUYd1ZVDvru16bJL6aY53tH///hiG6SkzDIP+/fsjMHWpVvDNOy88wb9fmscl191I5rABrXAGLUNtBkp9y6e1GdA/HazCY1RiBDzpDkTUZ/RYOOc1PvtwCRPPnMxZF1waMOLAQWgGBihal8umXC+pWWYhjVEJqYwcm2JWllMgJiEVp0Oh3K8zdHQ8fPNjrW1GY970s6ZeyEcL3wmUCndz/tU3cOkf/8r6nNWkZo1neHxKmDfYItToFUKwp9THwF5dQvYcLMldpersKquib1d3VPaBvbKyeCmpWeM5Pi4Jl8NBwtmT6OZ2YhgCh0Oxdel6tXNISh+H2+1BwzSwQ72qo+MTeGrOYj54dy57du/m7a/Wk5kcz9Sr/0LKcf3oiDQ1qNTr9TJx4kT8gWIpHy14m2fmLuG40ya28BFKJM1HGsiNpGhnaVCbGKJbjLTEKIkumpPBAWD//v04HA4Mw8DhcLB//36GCsj3BnWpmuqnKNdLZpQWALCwgs+qGyv1LZ+GGtBVVVXMnj2bzMxMKlUNd4gsQTUMYlMzcXuiU24ya9ZLzLj9JgDyV6/AEAZnnn+5uSpk5TQGNhXkcusfzMnPq097eHz2Qk6IS8YQgipN0MXlMLM7BPRW9dl+W9blkTX09NY+vUYxJimN/7yxkDzvasamZTE6MZVubofp+XQqVKqmfnhDQQ7vr89h8m9OJTMz0zaYrWvop4MVHKpSUXVBparbHub1O0q4c6mZ+m7k4T144pzYqEyPaa2sOB1Q7jMlJ1v3mDmtu7gcHNevuynBibBQGJuUyvNz3yNnzSpSMsfZ44pCoI8MwaeL5ppV9G66nm9/gNHr88g6LrquhVBWrFzFB59+wXm/bTkv94oVK1BV1X6tqSoF3lX8PsoN5OY4ViQdF2kgNxCv18uSJUsYljbRzAmKqVfcVJjL1x8soJvHyQ3Tr5I3TxSzYsUKfP7Iy6ANYeLEiXTp0iXMeCxBkJw5HlfAEHQ4nezd8QtFBTlMHHZWK55N87C1ttXslPqWT0cmZeAM0ay+9NJLJCYmckLqSWHGkmYIRsQl8+jrC9mc7+XyKWdF1b2xcNHCsNdfLf2QM867zJZSgGkgr8tZHTb5KcxezdAxSWwuzGV9IL3b6MRU21hUjbonykW5XsQ50WUUGUIQk5jKMTGJaLqwpWMOJVj8pKgwl39cPhW/38d/HryXm266iRLhIS41i5T0DDuLy6Eq0/ixMn8YQrB+R4nd1ra95fZno5FQo18Rit0PVZrOlj2l9mcifS8+Oc1cWXCaF5BhWEVRhBnc6eoOf3zL/s7GXC8/nXQiqi6ibvXR6/Vy5hmn4/f7ef7xR1pMHjVx4kRcbrddbtvldpNYLQNKtNFcx4qk4yIN5AYQeoO43E/yzJzFxCSmUlSQw61Xnmvf7Avnvsny5cvlzROlTJw40V4GbYpHM5Lx+Gn+VsYkpXL/y+/y+eL5rPhgPssWvc3XH75L8jFfRvG1IGpNw1bX8umxMQlccvmVvPbyLMAMULvhhht47KXZpJ98hr0/a3VldGIqSWkZZEbZUvK5507l888+s1+nnXpWMKd5SABufGqWHZTncnuIT89ic0Eud15zPppf5d1ZTzDj1QWMSUpj+6Eqrvvw5zrbjUnOiDrfqWXw6QK7xLbVESJgJK7LXo3fZ2Zh0AyDRx55BMXhwOPx8Pd/Pcj+/QdIyxrP2JQ0BOAM5JHWDVHD41q90Eo0YTo+FAwBWwpyKMrzkpo5njFJqUDAcCZyrIlVNkoIU1Ly3f4KqlQdISAuLQvl45UId9AQjknKQCAoqVKjzkBesWKFmcWjhUpLW2RmZvLc3CV8smgeqmFwyuQLiE1MjdrrAZqXGlPSsZEGcgMIT4rvpyB7NTGJqazPWYMWslzUmW8eTTdwOR3tfRh1kpmZybNvL+bnojxOn3Ryo36nTbtKGT2wZw3j0TKqhsUn80rudtQbzofHJ+P3C+6++27uvvvuqLwehAAUs3LcN3vLGHF43cUtLAwBF15yKW++/iqaZqYD03WddXk5pE08I8TIFGEZDKKNa665hu/3l/PFx0tIOeVMTp16KVZpaaEEf9eRCSk8/NpCtuR7zWIgcUm89swTaAGvsqrC+pw1xCSm8mNxRb3tDo+vmQ6tvbENO0PYrw3rAsEMVIxPz8LhdKCHSMmEYeD3+3nkrpsRhsHrT7v55z0PcfBAMalZ4zl80GD0CD99R0jrVRQqrfF4ePZtq5ogwdx1IZhSO9iYn8P6nDWkZY0nLtk0qk2JBTgQ6CHfGRafHLV9MXHiRNweDzSztHQkYpNSSUxNp8ynUaUZUdsHFs3J2iHp2EgDuQFUj3A2E5zDmJRMnG43WsCD3JlvnvU7Shg5sCc9u0T3JTUmKY2ElHQO7+lp1PcqVY1KVae7x1kjW4kQoOqCbc5B5gaHA2EYfPHFF6xcuTIql+QMYWZnQIGSqpBJnmYgEHRxRU7aLwSkpWfw7LPPcsMNN6DrOl26dCEhOQ0IeCAJepCh/VJ61acbPPvCyznl3EsoqdIgkOLN7BcRLCAiYFRCCinpGbgcCqU+jZiUTFxuN5pmLhHHpWUBDSsME425oQ0Bmwtz8a5cyajkTJLT0sNkEELA6IRUbrz7YZ68+1ZbXuNwOMx814FqeqrfzyP/dzNCwKtPe7j/6Zc5LnViDX+rIYLBe9GGNTGqLq2xqwkioFq6ux8OVACCDXm53BgoIvPa0x6em7uYuIAcb13OagxND/ue0QDNenuRmZnJog+W8sGny0jNGk9qWnqL7NcKfLUmZQDRu55g0pisHVKr/Osiuq2ZKMG6QZYsWcJxKROJS0pDFwbD45N58NWFrP54IT08rk6tQTaEQI92VwBBfaG/mmurUtXtB3loRadyn8a3+8oxBGzdU8bAnl04pm83ACr8mm1IqHrQszYmbQKbcr7GCHjYonFVQQTC9KxgU4ste0pRdYPEo/vgdNSylCwE06dPJy4uzn4YVHTtZ/dDlabz/f5KTujfHc0wMET9hmNLU59uUGB6+3K9qzg+Pp3RiSlhS/+WF9V8oAcyIwuzv0aOTeFfL85jU56XuPQsRo1NRhcCrR79sdVuNN0lQgg25udy8xVT8fv8KIpC2smnc/H0G0hISbevDwX43bTLGR0TS753Nb379mX//v1sWV/Aqi+W2vuzSk9rqp8NBTn0jh3Hpl2lYW0ueOlpDpw8kbMmncQRYVkvogOBIK6atCYpY1wwZ2/Ag2wZQ0eMTmLk2BQKsmsa1ZbXOT41C8fHX4V5kA3DnFBGa3rQ1PQMfj5URZ53FSf078GEE02tcHOMwIJfDtljpjVRitZJQigNydrRHK1ywS8HSTy6d9ReC50VaSDXQvVBIDMzkyFDhvC9r4udxUITMGJsMhmZmQzu3Y3j+nevf8e/MlTdYMvusqh66NeFpTGszuaQh3jyMX3svytUHc0wAhXVRFgQ1ubdpRBiSFmcc+2NfLMuG11Vo3ZVwfYOYhmAwe1CwLrth8L6wa8ZbNxZgiEEO0t8CMIfGssKt2EIgWoInlv9I6u+P8DzU+Po6nLQze1s8zzh9aarW+PlpsvPRfWruNxu/jVrHmOSUgNGsqlBLVd1nIDTEfB6KQHPpxAMi09m+NgUnA4Fny5wKdCtAaVyo9EgWLd2FX6fWchCCMhetpT8lct4cs77jE1OCxb5CASiWUZflaazsSCHtV9/iab6cThdOBwOdF3D5fYQm5jKHUu3sq88vHTK2888wruzZvL2ex9x7hknt89J14KlQR851sx3vTnPTO83JinNrvrmAL5auZrrL/o9qmquKs58cxGJGeNqGNVgjhu6MCUp1VuLZqdC7tps/nqJWfzktUAec6BZAWt2WkCCq0zRtqLSVJqjVbaKFDmlfRxVSAM5AnXNBK0lOCPgLXMpjqge5FqbKtXAr+tR99CvDUtaWX1QtqLVIy39WoUNqtvVoUbm3MLt9vY1vsO588X57N6Uy6VRWEEOrPM3O8M6j/yfDwKWRzm8H/y6YRt3FarGzhLo192Uqewt86Eb5jc0XfDffeWA+TmdLvb32tI5Up9u8Ouvv7K9faqmsCnXi9OhsC0/m6TMccQmpqJqBirYWQmsftpf7uebfeWkHtMHBPh0A6fbycCe9XtDa9NklwTSo/Xv0TjpT3MRAuLTslBcLsSJf4CchVC2D01VKcxeTXxymh2YZl0x9hJ5IPvFzLfeozB7FcmZ41EUhcLsVaRmjaffkYPZt/V/Ndq0PKzZq76OOgPZSoAohGBUQirJaem4HI5gXuzA+/neVfgDhUHAT+Ha1Vz557/z6BsLKco19epxyWn2NePNycGIOSWspQPlKoMOi970oGtWraxRbhtoVsCarXIXCroRLNDza6ApWmW/ZnCgwh/1OuzOijSQI1DXTNBKA2UtwRrQqQ1kCDEg2/tAGoCVnq/m9vB/w96zDYKghef1enlz8VJOGDma9JNP5+Ote+33lv93P0cmDuKvf/o7GUP7tuwJtBAGIb+ZIvB6vbz13lKSM8cRm5iGIUzDd0APD4qisDbby8KPP2dsWhZJqeF6xGBwmkATBpq/ClB4/avN/PPMBHp3dbX5tVGfbvDECRNwe9z4+x6PuOgxDnb5iX9fcwGaqjL3BTczXl3AUaMSEAhU3cr4IdAMwR1Lt7K/QiW1n2DIrrXEJKaSlJqOs7YZQOk+6GUVjRHsK/fT3eOkb/egMfxTcSVVmt7mBjKYmUbOuW0mi8sHQ59BsOjfOJ0uEtKzAMGmwjyKcteQnDGOtIxM+x6yTLvYpBRGJSTjcjhwOmBMUipup8LPv+yI2J7D6cTl9pAxfkKbnWNjECJYHTVYKTDwd2AlKTlzfFhhkIT0cQhhFo1JSEnH5QhOPIWA3F4pcEJ4O399fxNLr0lv04ljYxh34gTbIx5q8DUnYC0YuGs6mDRD0NXVMbzI9UlLmlJhcH+Fn+2HKu1Vu6H9urfLGCCJjDSQIxBpJiiEYOFnX7OxqIjkzBMZNTbZNjAiRWp3FqxyxdEadFMd6zfTDEGFX6O7x2VvJ0LpZTvRv8DWKFsrDD6fH5fbzRNvLq7Rjmo0TJPa1hiGYEdJlT0TMLW4ufz9sin4/H7cbg9PzVnM2OQ0PvhiBds35XHE4Ydz44034vP5cXvcPDPnPVLSMyipUjmsqztkqRS+WZ/Pvp07oN9gNh5SeOKTdTw6rX086HXpBjMyM3nw1QXMzfmOPCD3+72oflNmoKpQkL2as0aNBUxD0Mrq4NcN9leYQY25BxRyC3/C89Lj3PPyfPqfEB/5QL56Bc6+FTD7yKcbfH+gwjaQLc99e9xC1mJB8oRJLF66DZwuQLGTQW/Mz+XOq89D86u884L528ckptrBVgrg1wQOR3ASqSgBD6GIfP2f/6ebmXTyRJJTWybwq6UIBpCFTPpDfhPLKeJAIT45jWfmLKYgezUJ6eMYlZBie9gNIdiYn8e6tatJzBhH/6MG43d0AXw12ozGMcIiNT2Dx2cvZkPOai6cHCwWsmzZMua8t5Tzzj690atjVn8aQlDq13A7HB3i+dlQfXFTKgyG9km5X5MGchQhDeQIRJoJrlq9hpuv+wOqqjL72cf5zxsL6HtCHMIQ6FE8yLUF1kOlpEqlS0BzGo1Yy3kCQYVfZ/PuUlKO6Rt4j7B/w74XWE42DSVY9uVy/IGlR02DTxa/A0dNrdaWaYRHG1Wazq7SKooKctmc7yUpYzyF2avsAioafgq8qwD46yVT0FQzcMswDAwjaDwmpqbzzd4yUo7pGyI1gQ8++xKOOtVub9+BAyGyguhxlQkBw+OTOXKnAdth3+4dIAwUhwO3283o5Iywa8IIiE83F+QSdh6KA1X189as5xjwuxtqNrRjC2PSTqQo8HJgzy41DK+gvKe1zrZuDOv8bASGrrNu7WpACUtpZ6W4tCZEigJ+Xaer4rRlSApmAOTbr78Mwy+v0d6YyVcSO+zwNjq7hlPwyyEg+Htb/7O2iYDX3Pr1xySZemxdGPa9bgiz+qKlb3d7PPzryZdQlMjnG30jhIlfM/BpBrGJKYxNSaOry8HOkiqOOqwrmZmZuAePYviAhqWGDMUIGYODnmS93u+1N62ZC9kIOJhkgF70Ed2Ja9uRzMxMbr/9dvsm+OqrFaiqP/Cg8FOQvQYIegw6whJRa2EZkHvKfHy/v/5csO1JaJCUlXjil4OVQNBDVB1rAiCAQ5UqR8em4HJ7cDidOBxOPlk4t2ZDIjoDcBRFYX1eDrf+YSovPz6Dv136e3r16Yc7cD5mGsPxFHhXoarmA8Eqre1wOnG73SQEgo+EgOIKf6A/BRvzc8gPMY4BevXpF3WZGyy2H6pk+Q9WlTfz4TQsJp5HXl/I8PiUoBErBKoBH7wzm39fEz4RQlEQhsGmI8fz1a6aE+V7zxjFxCkXh2ypeY21Z98IISj1qXh/PAiA4nAGrgM38WlZJKRn4XK77d/eSnFpCEFRQQ5vv/gkRQW5drCV6T3N5fqLJrP8gwUR27zrk28C2Rva7DQbhGW8GULYAXm2JIDg2GGN9VaVvKAH0HyvcG0wm4Wq+tlUkEstCWHYXJgblc+OLXtK2VFSaf+uArN8uEVoGsfq+DSdn2rJCW5NrEInidHoSKiOtarsdDpbNPDauiyqT5ol0YH0IDeQk04yq7Cpmorb7aZ77z68/+ozjE0bR1wgkKWzUanqlFRp9mAZzeOcbggOVPgDGsLgNoBdpVX28rDA1Ma5Q4qeWIZz0HOUyrNvLyZnzSp+/t+3fPpeTUMgWqU3CpDvDX2Aw6Hi/Tz79mJy15jBVaMSUgBhayzdbg9///cDHNh/gLi0LMYkWlXF4Lv95baXzfQ4hi+bdz+sT9QZAAcq/Hy/v4I7Pt5Gufswc2PAWhs6Ko5h8cmU+XR2lFTS3e1iYM8ubMhby8y7b0UPFEexGDDoGPahQK/IHsLqRmDB9hJOPD68qqDlXG/rMWTFylUs+Ogzvu6RysaD5rbux45m0nW3kZ6WxvD4FLq4HNz+/Fy+LVxLcub4QNUzQVFBLrdfdZ6ZBcTj5tHXFxKblIpDQOHa1WEFlCIRjeOlZbxphsCw/JwB47CoIJecNSuJS80iPjnV9i6LQNU9QgzG+LRgiji328Ow+GS+3hfZQi7K9XLOaRPb5gQbge0xF8GsLhYVfi0YfFhNlyuE4ECFyp4yH8f2rZnVKTi5CLwOtLX9UBU+zWBIv+jMBNUUfXFDiRTjIokOpIFcB/k/H2TUwJ706OIiIyODGc+/RlFREf379efJ++5AU1Xef+Up7n15PqOjLhq79fnhQAXlfg3LKxaFzzybg5UqPxZX2FHUlrfop+KKaoO1YMOOEpKP6cOPByrYVx7wkAaWj63PjUlKY1h8MrkrPmPZx+/jr9bevt07EWJI25xcI0kISUfldrtJSB/HmKQ0RiWYwVV+zSAmMZWn5yxm15Y8hiWkc0JcMn7NQNVt0wEI6rOFMA0D1lZbLo2g5Wxvyn06hhCU+UKM3YTf4tj3Iyf97jyqNINyn8YtH27FocDcS5NZl7MGQzfgimfC9rXv6DQ46Q+1trU5z0v/k4+zX9/+8Va+uj4rzPtWl7yntfB6vZx5xun4/X7ERf+BgWYEWbmu8EHXdC6NT8avGzgUhWFxycQmptHV7bAnwoVhOX/hs8XzWZ+zhqSMcSSkj8PldqNe9myt7Ufj6kpwZUlgGNiBiBsLcvnLxeegqWbMwWNvLiYjIzMgMQmuHqq6mSYvJjGVx2cvYmPuGhLSx9O135E4Vu2J2GZsamYUCY/CCTo+gmP7sq9WsvjjzxmbPo7tPTxcOuW3YbrcvsePodxfe1Yje+wIrDqZAZEKAtOwHhJdFenDaIq+uCFY/WtE5Tpb50YayHUgEPh1gx6Y93NMfAJx4ybx7ktPoakqRvfeqJ5ubMz1MuX0zmcgQ9BAIsqMoEhYHhFnwCNSVJDD2zlrSEwfR0zAK2o9EAAOVKh2fkrDWmcl3KAZNSaBP86cx9PfhLe1aul7bD7WyRmjfttGZ9dwYhNTefDVBWzN99K7b38Ks1fjdjoYnZBie9ENIRiTlMpJJ46jtEqzK+RVN+yEYupN1y7/jKSTTq3RliD6Bn5FCWSjcShhyx7GqdczPD6ZXSU+/rxoo7ktsGwek5KJu4sHf/9ja+4w+fdQdiBiW/t2bUf96TsgqMvfWJBDekbwQRvUZbYdK1asMFN46ToY4ZMa61JXDYHu1+xjtNNyCRibloXL40ZTweFw8tniueiazhyPGbT65Jz3ud5buxc5GldXLE+eTzMgRCO7dOE7qH4zwE71+/ls8XzS0jNt76elH61QddyBlHCxiWY5Zd0QbPv+Z/xVlUTS4I8YmxJld0c4IuTCLMhdy+Xnnm0H8/72vGl27IKlyz1t8Cig5mqiEKZEwxpb/XqIQ0V07hzAIuQ/SXQhNcj1YF202V4vc1+dxebCXMamZeHsNwimv4648nliUjI75cVt1hcLPiSicdnUwjKKrIFoXV4Of7t0Ci889iA3XDKFjQW5bMjL4c3nnmBDfg5mYYugx8OAMAPZOt9Sn87T39Qc2Q3DYEPOmqiTF1he8lEJKcSnZfH0/Xfw6syH+MslU9iQn2t+JmTCY5Whtpdbbc+P+XrxnNe54aLf8cbzT3LTZVMitBcsLRttRNKFCiHYWVIVvg0YEZ/M3S/Nr31nPSO7vr5YOIeFLz4Rtu2my89lfd7asP1vyDevPa/XG/ZZTTfsLBctycSJE3F7TN25EuG32bYuLxCAHDKBIHg/jEpI5d6X5nPRn2/hlHOmoWt6QLJj5k+OSUyps33dMFCizHdqXaP+gCfYzP0d2XAJlSBYkq1Nhbm8M8vUZNt65cB7voryyG1GsWMh7ByFYO3qlWZwsq7j9/vYWrQBl9MVpstVAnnVq59TqU8zS3IHxpYyvxaYbIVovaNwjGgoXq+XGTNm1Lh/G4Kl3Y+2Z4VEGsh1Yt3oq1ev4YzTT+PNF57kliunmhXDLg8uH46MT47aQa61sYJWQpfdo5XQgTvfu8pcItbNQJqli97hhkum8MoTD/Hni6cw+4Mv7Ap6oQ8KCA3WgapaXGEOh5O4tKyovC4smyfPGx5MtG6tmb3CLrNMeB5Y64Fv/buxIJdH/3ULuqYhhIHmry40sfo7uuQ3ihLMHBHx/WqvX3rgdl68/zZEE2QBQhiI7ZvDtqmaTkH2asDMlrAxP4frL5rCS4/PYNKkSWEPWV2IVrmvMjMzee+jT7j277czZNjIGu9vyveGebWMEI8qgXth1NgUplx1AxPOPi8skC8hfVy1rBg1icbJtLUStv1QFX7dQA+kavzNlGm4PWY+cJfHw8mTz7fvD2siWVSQw7+vuYA3n3qY2/4wlaLCkMkmgm49ekRuM1ozIInQccA8j5jkTDOY1+FAGAZbNqwDBNdee62d9sweIxDk/3zQnmCFjqEC87q/+t0N3PbxVtsAj67pUsOxUsDdddddNe7fhhC4RBAQdZPGzo40kOvAGsI/+PQLcynJMFBVlfzAw83C6OCz3+YQ6lm0jOVoxPR2B6PQ49MycXuCD3UF7KpRquonZ/VKIGQCEHJatldZ1F4dbkz6eDs3ajRheW2EgJHJGWF9MDZQ7AARUkikmtc89HVB9ipTl1tXe0Rf8KYSkNgYEdJLRTrUL4eczxcL3uTua89vXDtbVph/lO6DDx+2tztcbrsMceH2QxRkr7Yz5IRWLLNo6f6rVHUMQ5CansFl199Iz569anzGSnNnhPzelqcr9DrYuj6Pojwvl//zHs6YegmnnjMNEBTk5dR5DNF2TZgIyv06N76/iZfX/sQ36/N558UnMQTMfOs9Lv7Lbdz/ygKGxSXbxrHVF+vWrjFldwEv+rq1a8KcBt2612YgR+/Seui9LhBm7ue3F5My7iSUgJGs6zruPgODlWbBvm5CvaLWCp41WghA1QU/FlcG24jWjqiHSCngGoqiKB1iBbazIjXIdWA9FNLHnWhmsMCP2+0mNiUT8oMX89Z1eSybk8vJJ09k2lmT2u142xpFUWz5gQj5L1qxB20URo5NtZPgj00fh0OBpYvmoaqmvi4pY5z9YLAKRQjM892Yn0Ph2tWMSsqg71GDI7ZVlLOazWMHcsbIs9r2JBuA9eAaEZfMDf/3ACuWfsDEMycTk2Aui4d6ie2HZKgHObCfhPRxeLp0we/3mUbnjYtrNlZtqTla2JifQ2XpIejeO2z78v/uY1b2TxG/U19mhlCuST8Go+sRvPG5C8MwUHS/bRwoDgeGMIOADSFISM+yxxeXu2YKqZZ8buqGYPOuUgb17kpXlyOg/aw5yzONQHP8u/WjLXR3O3nwrNFUqQYel1kwZcv6PO6dPg1NVXE4nSAEmq7xycI5OBwOuCFymjeITg+yJasAWP9LMflPmec2/8UneHz2IkYmZlCUa3rWx2VlBfT15rnEp5np8DQNM0VeahYAPi1oIEZCE1HqWgnIy8x0fMGqgGOS0rj6b7eyPteLqqqBKoJZwa9ZujvM8TKU4Lgi0EJW3owQT3W0ohtmEY9eXVw18hU3pcR0KHa57WjugE6KNJDr4cfiSo6NSeSZOYtZ+cUnJE44jWNjEiA/3/7M/deZA+nrzzzGsV9GrrDzayUsV2gUGkLhBI013TCD0OKTU7HKhz/11mLWrllJWtaJjElKM78ROK/NhbkU5Xk54vD+PH7PHYEiAG5ueuwVoKYHzjAMNuauRkw7k2grkGFgPvg+W/gWr8y4E13XWbd2FYYwOP/SP0TQBSq2VyjUmx6TmMKTby0iZ80qYmJi+efmCO1hfqe0SouaAjKKYha8QAyv8V5txjGAo3svGlrSoMynM+X8Szlh1Gg25qzhh4NVZAfe0wPe98RUs6R3TGIqz8wx0wamjxvfauOHEIJ12w9hCLPcdReXw5SaRFxHFPhUncvfWWdvMYTAp+m4XS6qNIP1uWtRB45A/LwBQxi2Ja9rWr39FIX2cUBiYR6YpmkhHmH4ZPE8Pl00D00109o9MXsxoxOS7fFhWHwy/3pxHhty15CaNZ7RiamAYH3eWrK//BTfoNOINA689/rzcMbJxJ5dM8C1PRGBpSS/bphls+3tEJtkZunYkLOG9HEnMiYpNex7Rtj4YaIE1vAMYeqPL5pTYH/HMKI7F3KlqrN5VykCwfABPendzR32fmNTwHm9XpYsWULqxNMZEpMY9LpH+dOzMyIN5How58EKY5LSOOLoY/D0HsCBinBPkjWQamrLVthpCeqrH9/UfS5ZsoSx40/lmJiEoOFkCfKiECVQqUwg0A2zDK7AYXqFhUAoCrFJKRwfl0Q3t8OOTBfAh+/M5pn7brMLZhiGgQhUldu8cT10Hx+hRUGv3v2ibsgTmOWDP170DssWvW3LDHRN46l7bmPU6DEMi08KCaBR7O8ZQrC5MJct+dmkZo1nRHwyMYmpnBCXjO/QPtj83xrtuZ1marCfDlbQze2kV9f2HXJ8mo6qG6bX64vIqbciMeG8K/n62HMb1ZYCjEpIYXhcMjl5+WRvMbe7PF1ITB8fMrkUxCWnMXxscsRJxPr8HL58u6BF7mHLU2dWSjPTcUWavl0xdx3PnhtX8/uAqgl0YbBpQDri/HTIXQBrF4A/WBzC4XRSl/gmGpW3djAu4FfcOE6ZjmP5LNxut2nEWQazz+CTxe8wKmAgGwhU3WBYfDJDxyTRw+NECMGG/Fz+75rzUf1+uGAwDIqp0ebCl5/mw5ef4Pgoc6xYXmOfZgRWGsyRzFp9Gp1gVtjr4nIQqrKyvcSB/34+WMnAnl0C3wXFEOwrC39+vv/q04yfcBJZWdFz/qHoRlD+UNt43tAUcKElq10zn+Smfz/Anr37iUvNIi45td7vS9oWaSDXgeU1cyrW4GneHj8dDK8SFFxaa7kKOy1BQ+vHN3Wf7plP8sSbizhuTJL9fjQ++CxsHa1hmEt8gYebLgSOEC+pETAarOCbp++91TYkDSFwOJ0IRcHldjN0dDz8GKExxUHpwQNRtZRc5tNY+uXX3PqHqag+Xw29uDAMCrJXcUJ8UsiSZ+D/A1702646D82vMvtZD4/NXkhcUhpCUGu5dbdDoZvHGTXzpo07S1BQGJ2QQp+CbA7WjCuMiDjpGvg+ciq3iJ9H4AiUXXYoMGbsWNiyHoBbnniNMUmp4UFLAXdb9X7yer12ye/m3sOhmmIUs5JkbcGKuoBNGzcQaj4bgfWE9flrKcheza7DTB01qedB977w6ZPma7cHz5/foqrGXkOOJQo9hoYh8Icu/cefycWje5OSOR7NEHy6aC6GoSOE4JOFcznt9xcwIlBxMSgZEKi6wOUwKMxeheZXEYYBtZRTFoAahY4VMK8VPVBR0LIMrZDRUEMYzL7z6aag2lo1MoRZnMmhKPTv7rGdKKLaU2LhC4+x5NWneG7ue6Qcc0YbnmHDaanL9ZFHHqGy0tJd+3j0rlswDIHb4+bRNxYyLAqLxnRmZJBeHVQvI4qAtT8Wc9tHW8M+d/vzc7n6xtt4es7iqBrkmhM80JB9qqqfwrWrgwEG0ffMszENXoFuQJVmoAlqeDtCJQSWebh00bywYC7F4eDGux/mqr/dxgOvLGDwCTUzAAA4HA7GpGZRtLM0aooi7CipYsWKFeZDu5olpigKbo8nkIEg9CFnPRQFny6eh9/nswMZC7NX2/q52mL1QvcTDazPy+G1Zx6nqDCXLp4uDf7eykYYxwB7d263hacOh0IXV9AzfFxMfEgwk2Ugh2u+7Xa/+gq1he7h0DYtTHlRZAlQ9fSF3++vYOu6PP51zQUseP4/7NsRIkfpGiIz6t6XKkfXOo8lGgObDQjkQA7y+6v+woixyZwQn8yEyRfYv6kpS1pjZ6nw64IHl33Ltj1lfLM+j3mznqJX335mdg+Hs1aRleJ2R51jxSJYDEhQVJjLa888zob83DDphHVNFW4/xKZdJYH7PGhEGwL2l/v5IVCkSQiBXi3zjzH1XjP43Rse/P5rY9asWbz33nth23TDCAvslEQX0oNcB+E2n/mQt6JuQ0lMSWfiiePp4oqu+UZo8IDL7eGkk05qsX1W+XygKPTq0zc8m0N0PfNsbC8dpvdKt2UhprfMDbbRaBrJ5gnt3xu+DB+flMZxI0ZTkL2aCs1gyeaDtbbpcighkdzRoUNOyhiHy+NG1Rwox8Rxakosw0bHU1l6kPi0LGKTUjlQ7sflVOxMFhvyc3h//ly+WDzXtuCcThdj04IV4TQR2UJe+9NBCn85xCnDI5dibkveXfolf754SiAQ003Pv9eR17iZ7Nn+Cw4lDQXz3gipXB6YTAQNY7CuufCUbgcq/Bw3Nh13oCBHUwKAQrG8elbrRiMnLncu3cZpP71rSw0I/c1DjewGpC6LNBloT6pUHZ9qsPy/+8K2C+DDeW/i/fwjevTui9PpxDAMXG534PoXbD9Uxe0fb+VAhcpP+0ooe+J8NFXF7XZzxj8eQ9n/Pf8bHM+mgzVP+LeXX895p58YVY6V/eV+dDsHtmBTYS7/vvYC1MA5PT57UViBEyujjyDoVLLGPSHMZZQq1fS8I0Cr3g3HxOFyu0nOiCRVa38UWiaodOHChWGvjz1hGDt//hk1UKHRCuzsjFSpOpt2lZJ8TJ/2PpQwpIFcD4JwD0/1CFaAq+av582LE/E4o8tAtoIHli9fzhGjk8nIaP4gnJmZycyZM7n+z3/GMAyeuf9Ojhg6gpjEFHuAjFYsr5UhCGiQA4azIWyDwZoUWfrQfgPCDbteffryj8vPRfWr8Ju/YYyaGLEtIWBD7hrGjYueQU/BjEK//sl5vLmllP2Ow0idNIzEwX3o6XGaOm1DhEWuFxXkcNPlU/H7qkJSNimcdd5FjE5MtR+EmzZsAA6L2O4dS7exZtiAWiP52wq7epyh4xtwHD5f67Xl6doNB+a15HI4wlYRPpr7GsaEDOKS02yjwQi57iyKK1RGjk3m8dmLKf62gFNOPrnJhpRP082y6eYoZq+afLO3jG/3RS5iEYkvl7yL0+XE8PSBXsF7Q3E4g3d+A35oO3I/CrCCsJ74KIcvdoW/9/mCt3j5gVvNF1P+DZefifONP3PVLfcyOiEVv27wRt4vdlxKVWWFnQ9c9fv50DeEXv2Ow+13AjWzoEw6/0qGHdOHHYeqGNS7bq97W/H9gXI03VxtyV79NYf27AzmSwcK165meEBaIkT46kdwJS58EmRtU4gsr7nvpXeJT05rw7NsHC2xAjZ16lQ+++wz+/WFf7iO40bG4F31NbEpmWZa0Gi5KdqYKs2ISttBGsh1EFgtCtGugqLU/BEPVKjsKvHRu6u7xnvtTWZmJunpGeT+fBC/btBFcUQ08hvD/v37EXagmsqGnDUMj09utEeqLRHC1AZaA7eum56RdWvXMCYlk4SUNHyasFMOWT10xpRpfLxgLrqm4nS76Xv4QPx+v6krrEOh5HA4iE+NzkIhM7cp4DCN2f0Vqpn6qzCXDblriE3JYmhsolmgQsC6tWaO3lDj2Olym8uuBbnEJaWyuTCX+/9xPVz3Vq1tRkM/JGWMw+3xoPp9GJc83qptrVuzgm1xAxk5NpkeHmdYWqsP3nqZT196mCffWsyI+GQgKLUIXYKxcsfGJqUw7tzT2X6oil8OVjK4T7dGH8/eMj+7SqtsSYAI1M/7y+KiRu3HQJA07hRyE68P2378mESSj7qFPn37s+dQOe/Vtx97ZaX9MQzBury1fLliBVSb8C5/b27wxXHmbyUQlBwsRjMMU3cbQqQzKvUb4I/sVT+iVxcEsKOkMmoMZEPAhoIcbrvqPFSfH0VRcDgcKEoghV1aVrBQCgCmpzh0dcJasQvLgoMws/vsKq3R5oj4pKg0kILUf2z1BcRPnz4dMD3Jsanj+P3FV6IZgmNGJ+DTDDQjugoqtTXRaDtIA7lOQnI5CtCp3QCMpmCs6lgz+U27ShncpxtH9Gq49jISEydOxO32oGkqLrebkUkZbFmfx+ZcL5NOmUjS4NNb5sBbiD2lPvaU+fBpui2tqPhxMzdfMRXVb6Zt+s/rCxkcY64CWJ5lgSAmMZU7X5zPt4XZxKZmse/HbwLGMeFLzICydh4ifRoAp5x1ju0RiKYro/p1agjB1nV5PPCnaag+Pw6ng6tve4DTz7s0kKN3nJ2j1+Fwkjj+FNat/pIP573Fp4vn8fBrCynI9aJnXVpnu+358FN1gw07ShiTlMZTcxbz6pMPs7b+rzWjQR+i6HM25Q1mZEIyDkUJk1gIQFVVCrJXMyzezDdc5jPT4IWOL1bZXoT58H3ng09JG3ciV0w+rUmHZQWgWhQV1F3MIxIuT1f69K8pl/muTCEueSqnJBzNgQo/7y2q2/C2g7+ihPzs1RiaVmP7D1sD5xFzir3N5XITm5xBmU9n67o8fvnffnAMAMDv6o6z1wCMsv0oDle9Qcu6EaxUFy0IAQXeVag+P0KY1URR4JwLL+OUyeczOiHVTstWPYYDoKggl3zvalIyxzE2JR0IBqHOyd/Owo27arSpi+jqg+rUd602NCB++vTpTJ8+nc8LttmTVVW3+lIEHBOi2U6sjoYVFB9tSAO5DoQwvTjWbFito2qYbj3JogwjkODc8u7WdQ6NYdLZ59C1azdOnnw+xRUad19zAZqq8u6smfRb8jGToygad2dpFapu4NeF/XuuW7vKXjbUVChcu4ZBoxPtID0DwcaCXD58dy7lqs4pvzufkfHJbCvwYtXlq24gn3PBxbz3oxnQJ4B5s54k68QJJA1umkHTGlQfhAxD8NWH7+KvMnMO6JrBKw/diSFAKz9EStZ4np6zmOzVKxkal8bmfC/5X31uyhSqdJ578P84/Ow/Q3x6o9ptSzQjKJ8Zk5jKKWf+jrV7W6895ZVrcOs+4tLMLA+KAtvW54V8wFzFGZs2zl6h0iIYSlZJ7I0FOdxy5Xn4/H5eecrDiCamBBOBySEKbMjL4W+XToHrG6fDvmnmG/R0KXy+oeZ77xXt5sKEo9EaMMSEps5qbxQFevfpC0bNQEz9vPth3m3wmxvtba4/z2FITBLb1udx/3XT8E+6AUZNAECgoF/7Khf617Bv1w6+qKdt67qMpsmCIQRJGeNxOBzogeeFEIKBRw0mJjHVzn6CqOYtFuaqnCVBe/t5N0++tZiUtAxb2vbTwci5TazrPxqxzk9BYX+5H6ei1EhXGSkgvq571Lr0N+bnsvLrr4hLy2J0Qgr7K/yU+3XiB0WWq/0a8WsGFaoelQZydIlmowx7wUgE05fV1mF6lC6P7Cyt4pu9ZbZXtLkTU2um/Mnid/lo4TwEsDnfG5JU38+nX3zZAkfeslgyGauaXnLGeJwuU3frdDqJS80EEUxwv6kwlxsumswH77zBlwvf4u7p5/PZwrfYvf0XnFZGgmrBSIOODlbV+/LTj3jzqYf56yVTyPZ62/JUa0VRFDZW8xpuKczh6yXhRpKu67z28J3Mfuph/nH5uQhg2vS/MWJsMjHJmWbVtADfbCzEu/zzettubwMgNGvCweLiVm3rij/dyL9fmk9sYgoKCg4FtuZnBz/gcGIVYrD6xbCOUZiV7jTdsMujF65dg9/vx9CDudabQmiZ6ILs1aaOvpEcfkJsnePcxl2l/PW9+mUb0bacfLC4uMaEF4CjY1Cc4cZQBW7W5hewKc8c9zBqpnD7/VU3MO6sqfW2e9U7heTltup6RqMRwiwGcsrNT+LoNwjF4bAz3GwqyOXtF2eyeZ054duQn8ubz81kY0EuhoDCwHVlZWYoyDYzU1g6ZUdHqyhIqJdcUFzp56eDwUB9v2ZgGIIJE07C4/HgdDobFEwrMGUsN142hXeefZR/X3sBW9blIYQZL9CZ+HZfOdsPVUalxEYayHVg3RjW398X+1iwcWetn41WrJu7JY7RmikbhoGm+inIXs3o5EwznZHTidvtJiljXPMbakEUzCIhPxVXUrSzhIOVqj1REEKgaRrvvvIsW9floQeMiHVr14SVFtb8fl6ZcSefL3wbh8NB2im/4eihx4e1E9q9Boo9Yfjq6xWtf5INxHpgWWxbnxeWxg4UnA4nhh5MP1TgXUW5X6OkSqP70FhOmnxB2D4MvX5Dq6Wuv8bg9Xq5/4EH+Wrl6rBgoqT01r0+41PSGZWQgkNR6NHFCSikZIVE6DscIISdItHyHOuGGThaoWpUBdywhoD41CxcbvPh25SUYJpuBKubYf4WiRnjcHsaHzPxzw+2cM8Tz9X6/mfbGlZ8pU83d7tWDjMCM5MKv8aBCpXEQLn5SIy65NYa277dtJ6Y5EycA4fC0KQa7xsIjhuTUO9x6Di49d/3sT6/8XKX1sIyVD73H4Pzque54Pqb+fes+RgCbr5yKrOffJg7rjqPJe+8wd8u/T2vPDGDv136e4oKcjisTz8Uh2Ia1W43Y9PGBTNbGIKyQwcjtmk6mKLvIVqp6hysVO0MMNUn+ht3llC4/RB9T4hj2bJl3HfffSxbtoxRY5P54UAF67YfqmXPwpSx2KuYKkW5piOlvZ0J7UG0raJYSIlFHQgrmCXw4929chd6LT9iNEVlVycYIS/YVeKjzKczcmDPOr/j9XpZvnw5I5IyOO83Qf2dlebNF0gdF5OcyTExidz14jw253mZcNJJdpnmaOOWj7bYf1/hXIemmppDYRh4ly0ld+Uy/u/F+aSnZwaMErdZBeuSx2HAEPQnAx4hHY6PSeCw2DS2fxtMC1UUEnzicHkgMGGYMOGktjnBBjA2bRxkBw3afUNPxDngcxz7f8bhdDLxd9MYMiqWN/9zN6pqBugc1qcfqi7454ebKanSuPvs81j+3tygYd2AtF4bCnJIbEOpibXS4fP7cbs9PPXWYmISU1AUiEtOg9WrWq3t266Zxj0vzCEpNQOP04FDwYzQXxOYnChOnC4X8WlZtowLIWwpiCFg254ywLxnRyWk8NjshRTleknPanwp6o07S0PSboFQYEygXPCfs2vqbutDP/XPtb6X+3NtBgGcOepwlm41tS0uh8KBcpX/7S/n+P49Gn0MzaVw+yEG9e7K7lIfmmEQm5TKuFP9rIxg328ZUFM+1L3nYQwfm4x68RMR97/zkI9/fhCh9jowfmg/Vv0QlHPomkaBdxWcGx1FMgwBmwtNg101FE65+Dr6dffw6ZvPhcnSvlz6Af6ATtnvF3yyaB6fvTcPQzdwOB1cd/v9xCamsCE/h+xVKzn6+JGUFGu2XjsUTRdR6UH88UAFa7xrWL1yJQnpWaSlh997ZsVVc1IbWk1v865SKlTNdk5V1xQbApIyxuP2uFFVU9cek5wRHA86GdbEPdqo14P89NNPU9zCS5JPPvkkY8aMITY2lpkzZ7bovlsUy3uMCAjoa/+oHoU/LpjeUwhq3QBKfTU9fl6vlxkzZuD1em3j4l//+heXTjkbb4hEwEodd+kf/8IzcxYzLD6Z3J+LKTCO4vdX/4XYpOgrlxnJe3lMXCoOR/jlr6sqm/O8dnDek3Pe57TzLoMjhoEz6G1zOp3EJGfWGMh6eoLzzcTxk7j0hlt4fPZi0lsgvV5LoGBmRAijex+cFz7MBX+6mbtmLaDXb/9C1uQLueSmf9tltZ+6/w62rcujpMo0pobFJXHtHTNMqYWi0JDxvK2T4NsrHQFZQkH2qmDMfSvfqqrfz6Zcr11Fz5RZhPRSiETFGlsMYRapsLzJdkaAwN8jx6Zw+fU3msZ9I9GMoDc6tCpabGLb3qsTT+hv/60HHAoHKhpYyrCFMfMXV4ZI4wQDBh7R4O8veeM5vlmfX+v7//hgc63m3pVpR4e9djmdJEZYdWsvg2FjfkCfHuCPCzYiBMSlZuH2mCuFLreboSPHIAKyFGEY/PDdNlS/GgjsMzN9FBXmcv1FU3jliYd48KZrcbsjr1p8s3FdNIbwUJi3lusvmsLrMx/i5iumsqGapz+Yx7zmdy3HWsEvh6hUq8smBLFJqTw2exFT//gP/jVrHiPGpoQ9pzsT0RbMblGvB3n37t2kpqaSlJTEVVddxRlnnNGsCMuioiJeeuklcnJy8Hg8/OY3v+Hss89m2LBhTd5naxF8WIXkcKzls5oRvTKLYH5fEXgdfG/bnjL+tzGfqb87047AveKKK+yAA6gZcNDn+DFccEUfBhxxJD8VV/L4V98DMC1hUNjDPVpQlJpZFE4YGcNVtz/AKzPusD2hTreb0ckZbCrMY3Oel5TM8Vxz50N8/mZ+yL4UTvrdNNbuh893hBcVKCrMg27HAiC69uKCa/9Kzy6uFr3x60slVB8RfxqXB/yQt1/wwfZdLC7axdElHvP3FwJVVdmc7wWPmdN5dt4v/OH3F3HU8SP4piCbVbt1foqw21Di2jjlXfUiOWPTx9mBNhuakL2hMbicDsakWr+NEsEjpNiV2I6LTcIXkFZYgQKWlxcIW9oNvYcbQ3BiYAZRbchZw+ED+nPgwH6g7uDKluKosu9ZvrgAPKYcoUo1AufWPu4yawx0KsGJQ20VBSOhGwab89eAp/FyHVe1ifnV/7irxmTlQIWf7/dXtEvhhMK1q/FX06dvW59HUlo697/8LvnZq4lLy2JznjcwuJqduakgF6fLBbrpEY1LzWRd9mo797immlI1nDXbfPzWPzH0xVfIOu43bXGKDSZ3zSr7+FUVCrJXkZSazsadJcQddVhQnxzhu9Y9awX4d3M7w94DwYj4FLoeE4PH6bBXejsbVgaLaLMboAEG8v333899993HZ599xmuvvcYNN9zABRdcwNVXX80JJ5zQ6Aa3bNlCeno63bt3B+Ckk05i0aJF3HLLLY0/+lZGCGE/qAwEDoVavcimRyT6fmArl2podTjrOtQNQUmVymdfLg+LwAVwezwQMC7Ss8ZjGAJHQKRX7tdsXVmo59x8vgfbiRZCteShTDr3EoYMG8WKjxZwyNOf3546ESEEd159HlogCtsXGuX/h+dxzbuFoaPG8PKOmuf484/fwyjTQN5dXGLLWlqKhqYSisQ3e8sordIiHo/fV8X85x9FST4HTrwSgO2HDbc7zel04hyRAT+Yn//sm33EHNGLlLhkXA6Fd558BQafWGf7bSFBqj55WLZsGQs++pTRSZkMH5tsGp+K+ZCD1pMB3fHcXGISU1GUgAe52qWiuNy4A5WztEDFsm3r8tic7yVj3ImkZWRgVSAzMCvvuRwBj3ITjkcIQIGNBTnc9oep9rK44nDAje8182wbxt7Xb+bTXkfBFaaBfMnbhXxybTrudiquZN8GSnB8aIyBLC5+HHWwAb80vm13NQN58PHDa+gvq1Sj3QyG+LQs3F08hPr27/vjNO59eT4nxCVzTEwiLofZcU6nE91KjycEZ069kB79j2J4oqnDdznNEvaq6sfZ5wgquw+ACAV6NMMw4yPOiy4DOX3cifbxu91uEtLNWIKqgEfYEAJHHWtotb4f+GkrVZ0bFhdRpRm8c2mS+fnoenzWQDcEzhY+SGGmRIm6FHcN0iArisKRRx7JkUceicvlori4mPPOO4/TTjuNRx55pFENjhkzhjvvvJP9+/fTrVs3Pv74Y1JSUur/YjthGAJDUSjz6XUayF+8N4+e45M46YQz2/YA6yFgEttR8qGs234oLAgITMPr8ssv5/TfX8Dny5aTlDGO3sfHsafMx5GHmYnsrWVaQwj2lQdHO4Ewq9I5om+iUP2ILKNhWHwy29yD+SJ/O0OUo9ByF6HZUdjVvtT3aK57/C32FHnBE6mV4I1dXGH2i1muObz1/+0v59g+3XA10jhobCqhUEqqzJOp7ZcxDINIYglFUTjt9xcy54fwY7U8IPm5uRhHjay3/duuOo/Yoz7j9IlBQ7olB8PaJg8DhsWxv1ylSjNL3TpQSEgfB97GZ3BoKA5FYcFLT5GSOZ60jEwcSvi997sLLiVr7ChGJaRwqFJldW4+z917G/rOb1n88kyefOs94pNTsQssCIEW6mVuAlY2DGsJHAjm824DNFUFEb7MXFql1SiudLBSpbhC5bj+3Vv9mOwA7IDMpTEGMsDiXxpv3P9tRE0DSDPCnSs/HKhgf7m/3dwtMYmp/Of1hfx1bVCfrmlmENlxY6zCNjBibArX/98Mnrv/doRh4PZ4OGXyBRw1KgG/pptFbhJTeWrOYnJWr+QV0tlbS/VKl6eLeV9GGYmp6Tz11mK8q78mPjWLMUmphP5atcm2LMd6dYeRYQi27CmzHUx+3bADcgVmGlanEsHFHiVYJaGH9utO/x4RH4JNwsouFW0a7HoN5CeffJLZs2czYMAArrnmGh599FHcbjeGYTB8+PBGG8ijR4/m1ltv5fTTT6dHjx4kJCTgdNa8IGbNmsWsWbMA2LVrFzt27GhUOy1B6YFi/Jqgq9tBaam/Tq3lF4ve4asX72dwl3lRZfDvL/dz8GAlmm7gVBRUjxPdgB3uSor3lqILwbFDhvDI86+yc9t6MjMzGTJkCF1Kqzhn2iWAoHjvblyVXTDKzBvi4N5Syg8V43Qq/GXxf+22Kov3caDSTTe3gx1dWrGObyMp3ldWQwNWeugADrf5i/645yAACzbs5Lg+ybjcLrRA4ER1heTAo46ilzoKvo/QUMidXYGHdd/9wpiB3djpqQpbXvt+dylGaXe6exo3EMbGxtoaPrfbTWxsbJ33haoLdpVUcUzfbhTvLUUB8naU1fxglx5w5t8RoyfWOB+ny03axFP5+L/hb1WVl1B1UGVlj2QYWf95aH6VTz76gDEjgqtOW3aXckyfbvTs0vxY4SVLloRNHpYsWcKQIUP4avlqVnu9jEpIZlRcAi7FwTHHDgHvf+vfaSM4sovB8Yf3Ys0v5dw3/Tx0TePt59z8Z9YbTMxKZ29Z8EpaYozCWLkSvaKEj8uPYvUvCkx7GL5+DbVgCd4vP+HYIUPwOBVKqjQq/DpOBRzd3bgcDna4Kus4kpoc3FeKU4Hho0YHAk+FaSS35ZMogje0rHgfBzUPOzzB8/nlYCWlPo0uvl6tejgH95UihMDtVPDrAlXX0XwNL7kNTZur/JzzOYkjhzB5+GEs+bYEgKqyg5Tu09nRzbxGvtttBvvqBo3+rZuLbgjK9h/k6MHHwtr/2dudLhcnjBxNZfFeO12oAoybdAaDjjqKTYW5jE5M5fBBg6ks3otmGJRVudDc5v12xNGDeWX+/2pt96pb7+fYIce2y3O+LoqLKzlmyBD6HXkRCgol+3fjdphyiB2uSg7tO2RLZna4g7/VgQMVlFZp6ELgcijsFqWUeVzmmLyvjPKDxSiKQnl5cFyoPLiXveVOend1s6NL5HzR7YkhBNv2lCEEeKq64GsBA3nLD9uh22H4dQO/brC9m7/FvdPNod4n04EDB1i0aBFDhgwJ2+5wOPjwww+b1OjVV1/N1VdfDcAdd9zB4MGDa3zGqjgDkJKSwqBBg5rUVnM4rMLDoSqVnl3dHHJW4nD8UmsuEqEoaJrKpk2bmDx5chsfae04S30UO8rw6wYuh0LPLk40HQYN6ssvWjGqLujqcpB+8hmkX3mh/T2tuAJfF7M0rcupcHjvbnYp1J/8xfg0ne59DweChkaX3v3p3t1DD4+LQYP6Vz+UdmMvh3D7NEKP9dn8A9wwfiiH9+yCq0s5YD4c9xrd+NeL89lSkE1MSiZ3rgvf187tO9m3dQt0ieDtcDjhtevhD2YarHe2lfH4yCEceWRfeoQYgb9oxRwxsFeNZPP16YsnT57Ml19+2WAN8v5yP07KOeR0cVj/bjgUuH3ON5E/XN04JuAPV8DTq0/Nfe/dx6fe1ezrOr7Ge5FwedyccdbZYffxDr2YPn26M7CZlR3B7Jsnn3zS9iBPnjyZH3/8kb9cY+rp3R43j76+kLEp6YGl65Y1kLv16MFjUxK4+aa/sSqw9KCpfr7+4jP+fNkFbNldBgT7/qP1P/HZ7Kfp9rcQCc8RJ+DyuEk/+QwO6384XV1OtHI/epWG06nQs4cHl0NhL07GDurdoOPaWVLFjz9uZP3a1QxPzOCBVxewfMl8Duzdy/49O/m2JTuhPqoZ5N379KNXn+4MGtQPgG/3luHs1Z1ePQWDBvVt1UP5wXcAAXR1OajSdKpUA0+3KqD2LBwtQe8jjqZ7n8O5POtwlnxrxjZ06dGbXv0HMGiQmd1hu1aMQ1FQ9dbvh1CEEBT8coge/TxsrlYO+s4X5jEmKQ1NN1cyrF+yq9tB4oTTSDrpdIQQlPl1nLqBqht07eqip8dFV7eDKtUAajeQj09Mp1e/vnYfRAvl7jLUUh/Cp6EAvbq48LgcGIb5DP1vZRe6uBymRKdnN448rCs+TafYUQGVKpph4HY46Hd4T/p2c6MZgt3iEGV+jcP6D2S3UQaBCA7PYQPo2sVJrx5d7HsimtANwU79IAI4vE83e0W5OWzZXUrvAQOpUk1P+pFH9W832VUk6jWQ77nnnlrfGz16dJMa3bNnDwMHDuSnn35i0aJFZGdn1/+ldiAgs6KkSmPrnjKqtNp9BpausLE5StsCK0o+NFjvv/vK7RQ0kbByBxOyROTTdH45WBXI3RoskWnxTuEOrs04Nqq02JWqjm4I1uflhm3/Zl85L6/9iavTj2Xz7qBXVdc1ivK8xKVlMTwuCdYVhH3vxftvQez6L9wYaTlQgeJf4L/ZMCyDA+X+iNpbIUzNXaiuu6H64tBUQg3BEAHNeEjgV0MRR43C2P0NRTlrwJ0V9t7ijz/F+Oo1+EvDDOT7X36X1PSMGsfWUlia49DJw4MPPohfDQbYFK5dQ3xKml0mtyUxf1NBxZ7tYdsP7NuDopiBen8aofD8N5aWWKCpKpq/ChTzQXPE4KEkTb7A1sTamSwI6mSFMIsT5P98kC4uB8f06UbvbrXnM1765Vf89ZIptobymtvu44v356P5VZSBx9f6vZZGcThwle/H44LywMp9sOakyaEqNVheu5Ux+9RsyAj8dvt2tb73cs7j9zBsVEx4UJ4S7o22YjnaSoN8qFLF5VACpc4F3h+K+den28I+Mywu2V4FOFTlp8xnMLh315A6AcL+18pvrQvzP0OYhZfqwkzzFp3YMTwoEbLMBI/6l0OVHHlYVzbuLAnI1YLP2x8OVFDczc2Qvt3C7uWr5wdLUl76diHzL62ZUzuaaOmhM/QSN0T0Beq1i6k+depUYmJi+N3vfsezzz5Lnz592uMwGoTADES7/4u6PU4Tzr6AR99Y2KTMAq2JlXnDzoEa2H6w0m+/rj4Y/3dfOXvKfMHvBN46VKlRXBnUxlUvW/3hlj1k/3gwqrJ5bN5ViqqLQGBWOA5F4cb3N7GrNCgHqayoYP5zj3LPtRewZV3NNE76hY9i3Lg4cmMOBw6n09bV7i33R0zbIwT8b38FPxRX2Nsi6Yubi6WDa/LAc+HDOBxO9u7cXuMtI/43cOaNEb/Wy6Fyrj+bzP7B62PE2OSI/aAaRt0l3BsxImdmZnL77bfb9+BRMSm43R67gE1cILOEVle+xiZSVVXFF1+tpHh/eCLdkUMH43QorM/P4eX7/hl8Q1Fwud14ugS9MHt3/MKnC+Zw0+VT2FiQa2sSwbp/rbK+5u9Zpel26r3ayPcGsgjoZtGX1Z9+aGvs9Ysfa6nTr5O+ShXTrr+Ff70wlz+ExHX7NVHjAQl1ZwPauruUQ5XN04/rhjXqBdt76Mv/8nnkGlAtiqr62ZSfHRquEDGupbbMCK3Bt/vK+N+BCvva+uVQzeX9twt+sVPz3fzBVv75wWYWv/oMW9bnhRl8QsC9n32L94diNF1QGZic53rrzjvemPu8LVEUBSEEuhF8hkJoRqigEVz9+rUMaet+LalSWfrl17z+7BNs3bAOPULlxjK/TrTmsghOglpwnwSvdSFEq4zNzaFdCoWsXLmyPZptNEHjsv7Ppp32O2JGHd7ah9QkgjexiWU02RWORPjMsLjCj0NRQi5ewf7AtuBsmhoeZAA9kAMzWrAMw8PHZEBR+HG5nZG9VYaho2lQkJ8Hnobnij12eAwTbriFvL5pbC3BLvxgDaaabvBjcaU9aPq04AAZmpasIaVK66O4ws/eMr8dRb2xIIf1a9cAjct9K4Rg2aK34cbza745LPJksGePnlxw8fXMW7cD9u8K7Cfy/neV+NhfrhI/6LAa71kBIbFH9qKru/GBK2OSUnl+7nt4V61kdHIGMYmpIODr7/eHfW5ADw/7yhuXj7dbxV4qd/wPhplp0g4dLGbGn6bj9wcmW4qC222mTAQzXZTmCxoeRw4ewl+un8/T33WBwHcMIcDydmevJik1nXK/5WdVKPPr9Olm3oPmg7v241N1gw07SkhMH4fb40FT/TjdbtJP/S2bC9aiaWYAaSiXJR/Nm/k1J0PNYfxx/bg2/Vh+3NSFrz9YwPKvv4LLngFgy8Z1nDAxuDJhBc7WNpnzawZlfo1DVWqdXvO6KKlS+XZvuW28WP9b8d3++r/cQoxJzQwLCDSqVZGzApXaYhzdsKPEXpWw/nZGWGn6eOteThl+OIN6d6HUZ07K3n3uURa/5ObB1xYwOiEFBwqqIdi6p4yte8o4suwHthVmkzX+RLr36gMVNfdrEa2V9CwbQBMCJeTwgoZy8DkqBHyxYiWLPv6clKxxDI9PCUsVW5SXw58u+j2q34/L7ea6O+4HRoS1ZxDdeZBbOk9z6GRC1Q1+OljJ6CNaN/6gMchKenWwqTCPlV9/RWxKJtQZogdahNlgNKAowZtYN0Jna+FGsmU+G0bQs2I9qAyh4NeMMIMaQI0w64+2m9s614eKah5YxKpfXXqgpEzBse8H3m+EcdzbDTf+Jp3hh5/C1s+/gRJz31YuTIAKVbc98NWPJpJEoDn8fLAKv64jBKwvMBP/q34VavN+14LerTdc+2qjviMg4EUPnmWkbB6WkaJFyKZgGTKGEE32LgnMCnbHxyVR6TfTZlWoOjOWha8GjTy8R6MM5MwhffG99zwFPcfa23xVVQi/H4RAcThIHXcSV/3tVvt3TM0aj/ull+2gz9i0cQyJSUT5LlhtTXE4zMBIp5OEQN5mXQicisK29XlsK8hm3IkTiEtOw0oUs6fMh2oYNarRWZkR4pLTePKtxaxbu5oh8akMj0vmmBNGUpTn5Z2Gd2WTOayri9VL5vLKQ3eaebW7BidCWzYU8tvqBjLm77a3zMfhPcO16Rt3ljR7iVfVrUmriRHpZmwtND/CMPjp263EhUgs9JAx16K+wyqu8NPV7QwL/m0Kfl232zEzpQj2bI+c1XzNZx/yv3LAZUpzjHPuRP34PxRmr2bU2GQ27i7llg+D1Urvv24amqqyYNYTnHj2+XBs7ZLMUp+O0+HAp+l0cUVTFgfzelE1M4bH8iLX8BYD6/NzTDmT38+rT3t4fPaiwMqZ+aF87ypUNZgT+qvPPob0agZylD0/Q7FWGAC2H6qizK8zbEDzqmAKgvaJ3xA1stq0N9Gjho4ypj3/OX/6dDvvPPso90yfVu/nf/n+u6hbGREhd68hCKS6MjeYxq9pLW/Iz2H2czP5YsVKCrcfCpvVhRnPgddFBTkseOMlNhfU1JW15fOmITTFYBcT/sBxE89t1HdmTUtkUO+upt40ZHt1b1gwJ3XNfWRmZvK3f9zMgGFxjT/oCFgToALvKrtEbKPpd0yjvxJwBIZvE6bkpDREEhBaLa46tmaepl9P9n6tiSBEqGhV39S3Jgd2/syGVcvCthmGgZVb2O3xcPXfbg2kajNJSEnnvidftF8ffuTRmGnuajuSoFfqm/X53PfHabz9zCPceNkUNuTnhPVZbdXorPt1TJJZhe+Y0YkIYPjYFH5/1Q01Pj+kb7dG9ELD2LFrD6/MuNPMlSsE+INuxBNiE8POwzIThRD8WFzT3dhS3itbciTaUMpwcBe8cBkA2Z9/FPa7RzSQA8eY//PBGhPL9TsO8d3+cn4+2PwMF6ESOgFsyM/l/TdnRfzsopdmss4Vols/LgXHsHSGJ6aj6YLC7eEOB0210mWq6PWYGo9//T9W/HcfG3eWNOd0WgHFvkYMASU+qwaA4Ju9ZWEOkIIwA9jPurWr7GvNEILkzPG25MvldpN8cs2czyLCtRAtWLaAdT0erGy4U6HCX5sULHjxCQGeSMsX7Yg0kGvhp1+2g7trYLm9fs3bopeeqjcQoa3ZsruMn4orMYQ5CIdqUS1ZRVFhLn+5ZAovPz6Ds888g/X5awHsQcAK8LO8xxsKcrjp8nOZ88br/KuwZr+0pXauIVgSkcby3421l5GNhEMxywk7A9IUi3dfepq1a4NBqLbnvpZDOlSlcbBSjfhgbCxWW4kZ4+wSsY1mau1BurVRVnKIZYveYtu64P0ghKDcr/HLoeBD3fYe1HKadfVTQwhqA4VdzaoqgoE8OfbIRqUW2r1rl2nw7QzJCBKweGIS07j3pfnEJafWyKubkhIsTmIIM9/tjpLQPOJmP5lV9lYjEHz13T6+KNgaZmwUZq+yz8naV23nH/oZIQTb1uex+JWn2RahTPK2D19n/PamZSaqjQP79mCErBA4QoQdQ0bGhN2btleOmr+7PQ611OBiGz1ttLTvKwO/ee1nnvbbsPzfP9fmXBGRJwWqbrRYP4T2tRBmER29RgL4ABP+UHPT2eczLC454mqiY2iCbQwuP3Zqvcfy08HKqFuBVMAO6jVEIM9/4NoprlBDxjBhjrO2AewhIX2cLUcUwlzNemrOYq6+8Xb+/dTLnDT5whrt6dHWAdWwqv0FV55NNuwoYV9Z5NSuqm4EsvjUsr8osxlCkQZyLRw7+GhwuMyL3VW/298QsG7tmjY4soZTHpi1Vao6mu2NszxT5r/r1oaWAvWT511texUMggNo2CzZryKOGG6WKK5GNCwRlfs0vttnpm1ralSsiD+jUZ9f+v4C3nv1abasC58kzX3mESafeQaffPm1ud8Qo636oe0sqWJPma+a9KV5GEIQk5jK47MXMf2m25u/wwZQvmYBL953K5tzg/dDZOMmsFwZYXi0yo82h9AsED7dNLDUCPsc1LsrMyfHNHi/DrcbIQzIWwzv3W9vd3s8JE04lU15XooKcmukGXa7QodbwV2fhqfcM7NdhEos4KW1P/O128xfbAUbjk0fV8OwqY3g5wTfbsjn3unTmP/co9x/699qfHbB8//Bu+TtBvdDQ+jV73DcXTwoDgdOl4s/3fWQ/V5oNpH1Ow5h+fktjX7YeVhjUjMvCkUJWZmwJ/9tgGL+9ikTz+DMCy5j67o8+60FLz3J5sJc8n8+CAR/z1ApSCjVY0aagzUeWf0+Nm1c7ZXchibW2LRiyTy+XDQHoEbFOO33/2ban27mnpfm1/hexGOh+efl9XqZMWMGXq+3eTsiYPRV+CnIW8t7rz7NN+vzbYPYmnADthEck5jK03MWc83fb+fZtxcTk5haI/4nNjGVS677GyPHJNjP51A0PXo1yKHOBvN1YLsQ+DSdUl/tK5S137fByUc0nrjUINfCsYOPxvO/Kn7/p1v4tEcG/nqcyE63m/i0LPJ/PkjyMX3a5Bjrw7rc7Nl9yM1qDYxj07Jwe9yoKrjcHpLSxwWGy2AQUHDpExLSTW+kv5qnNNho+08HiytViiv9lFR5CNgGjafv0Y36+OtPPgyH9rDwpZkcf9s8rOVya+Lx5fLljMvKCk5QIuxj+6FKO81Vcx/c1gSoqCCX9WtXk5A+jkv/9HdeeGZ1M/ZaP8r79yO+y7GOIux4Ihk9of9WxxCi0fKHsP0TvBw1XfBdUT6ffvg+HH1O+DErRPYgP/F7+Pt7NTYf2GtlqhCw70cAuvc8jMtuvZ9XH7kLXdV45/nH+N0FF/PP66fbOuTQJiI6DHsfYRZtMfz2Q9jijhfe4ZuABnlUQorp9QzsTwhzCbO7J3w4t5dEA39vyfeixpyGOPnaGgF65ucNdH/LFijo2bsv9740nw05a4hLyyI+OY1nXzavDz1gbeiGCM9kUsf10BJDi7WSZk7C2sggURQ8Xbty9hV/QgE25q4BJcM+nnU5a8jMysSnBYIyLQdFhHHAOt7C3LUsXJ/TrJiF0HM3BMQmpTB0+Ci+a/D3Ba8/fCfHjxgNrpr1DGJSMhkenwKFeRG+XXNfzfktGpoqs6H4dZ2N+Tncfc0FaKrKYrebu1+ez4CsLISAwry1FGSvJi3rRGITzQp7sYmpjBybQheXw0yvifX7ieCqLLBuVwX3r66ZF1oTTVvxbAssG1YJ+Rug4JdDCMyMRFWqHjGg2hBmRqmh/bqFjVNBGyM6kQZyLbgcCn4DnOlTOVQYOT9maAnZgb+7gWNjEqIqj58QIBQzyMmphM/QrRs1NuBdLFy7mn79+pOfvRoDGJucZkbLExykBYLYpFQeem0hS1bmsTxSm0THxW4I+GZvGW1V290Q2BkISg4eAIdZKMVabuvbrz+PPfowR8WkMDw+CUK0XBZCAIo5Q1cam7S4GopiymH+cfm5qH4Vt8fNE28ubtY+G4JDUbD9CKGR+tWuiuIKf53eIjO4VNCY5M3VC62Y/Wte+N+sz+Oea89Hc3eH64IG8iCjGEUBhxJhMa0BgbcOxSzjfuTAwynbswFNVRGGgeHXWfTW63z07lyWL1/OYceNCZNcrCzcDI5qRSAGDIWLH0d/40+sW7uaMSGBXMPikolJSGVgT08gAA/7RhMItuwusyfmIrAUHOqJ1Q1B4YBxiJPryVvdwuWnzV/APG+FcC+jpb21St6HeolrSCwIXf1q/lFZl4aVA7m16Xf4Edzy0nyGxCbhcCjEp2ZBntnXDpeb+NQsU/K2szTMIRGUgAT7TQjz3v5bIL91c4zB0D4VQlBUkMsPWzfCsac0cA8Khm6wKc+LM6Nmppv7/jiNP9xyD1B/zYTmrhBESpXZHAPZEGaaREvepGlQlOulT1c3Hy98h08XzUXXdOY+/ziPz15ETMBIttOhEbJKYV/bpoxi097IE1HdMKLjAVoLtmQkdFvgGiqt0ijaVULKMcFxTTcEFX4z9qlC1Sjz6eEGcsgf0Xja0kCuBXdALD63FuMYwLH0cYzf3ATAjgrB82t+5IGzRtX4XCQjrb6qaS2FEGbqNYdiVvvRDAOnQ7FvXgGMTkjBEIJ/XjE1YEx5eGbOYo6JSQyTWFgD2OiEFDbsKIEIGaGiYX5g5f+1buS2mLQ43W5EYAn8sD792B6INbngTzczdNBA7r3jFlTVTO/z6OsLSUxNr3VAEAGzovqDsaGUVmlm7ueQ4DxVNeUxkN7EM2wYRsCoVBwOhoyK44fA9tCHn18z+G5/ea0yknKfxqFKjcacfiTvkThihOk9NgRffbAATfWDp6f9HWfeIq6/egpAzWXltbUsCx/aDV8Fs3qcPeU8BqQdQ+Kg3mz+ogCH4kAP8c/6fD5mz57NTfc+GmYg76puHFv0ORKX283YtKwwXWfog8m6ps1UjCKsb1XdYMehKvaV+8NkGJoh+G9ZLZ35v1z4/JlAQy1rIMe7irnr2mlofpUFL7l56NUF9ntWdpJQg1hgek5D79mDlSrf7Stv9vL73jJfIPWhaag4FSuLSuuPD336DWDU2BiqNLOy2pikNMgzYxN+e8WfGJ2YGiY3Ch1za3iQMe9tv2rmt26OMRim+8ZML2jodefWDmPUBJzbN/FDj+PxFtR8IGiaSvYXH0Nm/Qby7h2/IBjS8Lar0dKpMoWApIxxiEufgAO/4Fj3Ab169+Hvl03B7/PZhrBZhGg1oxNSECKYHrUoP5e87FUkpY8jNT0jaDAboCiRrzl78huFWPemNYpYk6pgbJNS4x79+WAl+8p9AXlGzfHHWjXw1ZELvz2RGuRasOqr14Wmh2tuygJJ0UPRDbN8ZyjWw/yuu+5i0qRJLaKXqg2B6RSyLmIr9W6od8nA1E8HjSk/+dnhWuSN+TnMfu4JNhXkIgT88N+ttbbXnkbyjkNV7Cn127N1M1VW67fb/8jBnHbuxTw2exE9eweNnyv+fCMHiw/gV03PhqqqrMuJrFW3+i40g0NT2La3lHV5a9m14xecLqetXR2TmlX/l5uJbegYBqXFB+ztVuVGn2ZwqErFSq9l6R9D+f5ABbvLquzAsoZQ3Xu0fPly+/r1hw6+IZ7i2LEJjBqbgoJSQy/M6rfMfx+vVjZ+4b+gOGgIdO95GKeNOJz9323glUf+hW7oNUoqA5zQvwc/bS5s0Lnc/dJ8YhJTqfAHx5dV82axbV0eunXfipqGJQR0k+X+QLBP0JtVZwL+0n1QXtygY2sMtw8vp2yr1y5MoqkqG3ODY52V+9Yy+O2xw5rcBo69wq9TXf/YFHaUVFGhavZ+BKY3b0NBTr3fbS6VlZWgmDpzjyv8eus/aIhtKIQGLYXmUA/FDrx1e3A6nc00BoMGualBzmrcitvxqRz+x2fwqkdGfNtx7FiGTIwcoJd4dHju88/enc3G/Kb/FlaqzPvuu6/Z8gow+yQuOQ0xYCiMGI92wcOUHiw2Y3AC16ESKPgzNi0rMLEzr9vFc1/nH5edw5ynH+a2q84zC/+E/K7VA3gtdCEaPOa1B+ETN9O2CV3VESK86Euo57z6aeX/fNAeuKq0lgs8bUmkgVwL7nrSjRzhrITvq0WCC2oM4JEGudaomhYJ++I0DPSAkaAFlnDsizbwubHpWXamA7fbQ6KtRRZsLsjlhkum8NLjM7jp8nMpKsxhyAkjIrbZUjrBprKjpNJ+AG7Iz2HO8zPZ2NoPwPVL2bPRy7L356FQ0+mZkD7efpiZFd2yapWi2A/GCANKgw8nL4c/XzyFD955E4Czzr+UR99YyIixyU3bYWMIMUD3f7fR/vvfn36DwJT7/FhcEeYZq36eSrWCNA3B8h5ZBsNRMSm2d9WvCyacfR507xNW/e/Ant0oDlMA0M0VeSh0usIX2RwhAbsuj4cxKZkowJb8bDS/CsLUtloBdx6Ph8svvxyHQyHf27ACSSPikikqyGXerCftbS89MYP7r5vG+rycgNcm2IHWPby3zGePPxvyzNSNRYW5CCLnmrZoLQHSY/+6jcP69sXhctr9sWfHL/b7lsQi9FoPDaALvS7sB6yAfeV+tuwubdIxGQI2FeTyzotPsqkgF90gUECndelXtRswVyo8TkeYgRzMNBJ6zta2SOOAGXj7/Nz3+OM/7miyMWhlZAg1kkcnphKfNq5R+ynRa8+Oo025myW+oRHfO/Z/1VIl6joF2c2LkaheTbM5CMxnSCijkzPt56TL4+H08y7lnpfmMzpQhEgIQVFhLo/96xZ0TUMYBqrfT2HgvAwhwifs1YhWD3JxhZ/Nu0prjNvBgL3gNbyuWrq/0NSmB6tUuxJmMI2nKQmLRqTEohZc9aR9+q22ntfUympanPDBzCxRWTM4qaWXgurj+sVFOFB44fw4M0WQ22l7KR0oaLogJiGVx2YvYkPOGpIyxhGblMqBCj8OBQpCMl2oKmzIWcMxJ54DO/bU33gbYy2dbizI4W+XnmtWLerWHf44p/UaXfY8AJrfz6eL53PqpbeQE4hIf3f9Tv6QmsLzc98jd80qxqRmckJccmDiEn5hhM7CmzNc2OWFDR10OPyowcQmptZblrjF2fgZ3U+6lAq36SmyvA5Br0LkgEVL976xIIcNOV58Z5/OqRNPrLOp6oVWXEePokI1PY+abjBibDLHXvkAP3UN5nU+fvgI2zjs6nHxwtQ4rltoGvWKw4FDcXD2ZdN5P6SdS67/B7s251F6YB89+w2wvYKxyZks9rhRfQIhDLOUtNPJ008/bT+sx004CZbWb9htXZ/Hvddfin/qgzDA3GboOhrmEnhMQqop4rAnEAIhFH4srjB1rAU53BAoWOD2uHl89iIGjhhba3uien7sPf+DgcF8t87lL6Kf/Md6j7s6ftXH91uK7GA4XdP4+N034UbTo6hZD9eQ6yL0mrCuC1syFTJhKvM1/lpWUNiQZ6apVP0qb7/g5oGX32V0cgYE53Jk7f6CNUec2uj914ZrxUv87c4/2ysVCmY6SIu+3dzVvMYhq0giaHzohmB3qc82muOT04hJTCV9SC1ynTrwaXpA74zd0daq4ujkDNZ93/ARqELpUv+HIrDkjedg+mv2a4fTSUJ6zVWutoojqc6G/Fz+dukUuH6evW14fDKPvL6QwuzVDE/KYOTYFBQFdMMctwwhWL92DUaIEexwOAIp36wVrdrcI6Dr0Wkgl1QFV16smALrLMJWo6s5CcPljma57TKfRtLgPgHJRnDlJBqRHuRaqM9APqxP3xpLId9v2xTmrdxZ4mNTYNYVSksvBdWGtSxZ5tPNBOci+KCxqnQhzMFSYGqRL7nuRsYkpSKEoEo1UHXBmNQs3B4zVZOiKOi9jmDupoMR22yzvKK1YDVdkB1i1LehvklRzEprJ59gBum9mvMzRuBhdsWf/x7QqZmfNTD1uNWx+rCpQ2VShlle2GF7rDPx6Qb3fvZN/V9uJr36HW4/zByKQmXuEvu9Dfm54RIS2zgWbNsTnidzfX4O/7xiKq88MYPfnXkGy76q3/sa6j0KyhDMNr7ZWx5mHAPEJSTbBrJDMUtO3zzxeC4/TnDedf/k/tcX0bNX77Dv7D1wgJPPOZ+CNcv5ctHb3Dt9Gt+uz2NkQgozXl1AbHK6WREv8Bvu3x8sYZyR0bD7vCjXi9pjIAwYErbd4XQSm5oZNCYD2y0jynrI5HtXhUxoVdatXV1rHlIAjo0Pfz3n72Ev9R/XN+i4a6BpHDqwF10LGuAixJNtVe20jj34b82VOOt87XNu2hGRn73alpJpAbnTkdUmD7fedjvzLmu51ZbJp59MUmq6aRwHLrgwPfr2nwjNcABBoyPUk15SpdorZPb/mtgRuhGUrBQV5PL6s5Z8TnDkMUObfK6NQQgDchfar08//3KzHHwI5T6thkSxrSjMNu+j6owcm8IF0//KyPhk8z43BLph2BrdselZeKzUhk4X197xILFJKUExWYiRWR1dRGeQnqKEyH9CrslQyYX5uuZTKyiPCjpGKlU9+B3a12aoC+lBrgW3s+65Q8mhgyiKA/HFc3Dq9QDs2f4Tf7n4RuJXLCczMxO/boQsl4fPgjMzM1s1OM8i3KMd7pkxDIFwhqQTUqyLVbH1eQ4DRsYn87e7HuCxf9+CYRjM/cmBGBC5ik5bBLzUh8DM52mnr/N4aHjNn+YxMjaerm5n2PWjGwbgDHqJAkOlTzXYuLMkLC1g6G/U1DEjNimVZ+YsJmfNKrr06s2G3DX8WAGFO1p5oqD5KC36GoTA4XCYAXtV5fbbhdmrSE03gwRDDTohoNQXzKOoKFQLMPTz0WfLmHRS3V7kUEKNb4HgX59uq/EZRYFt6/NYt3YNCelZDB+bQuLg3jgGJ6NmJNKzS2B4XHkAevYDoHjfXr5a8q6tq1VVwbsvPk76qWfhLz1EyoRT+W/ROju7QOjqUH2TbouY5EycixYS6iNVFIUJvzufEWNTgkFmIecZ6pVPzhxvX/tut5vRqVncFOH8bfofy+nnX4ZhwIjYeFZ//iG2Sfz5MzhLdlFnDcZ1H0HCb2tuD5yu2+PG5zPr3Csh6SG1gDUcKiOwJ9giPEjVGkdDA4Qaj7CL5phpLd3EpGTaqwYWJVVqxFRVTWX7zz/y2jOPMyQ+jeHxySgKbAnJg/zuW2+Q3FeQnpER6CPs5foaxoY1ONieuaaPtwJTbnJTSKabyQ+/y7s/ttEYrqs418xBTzVXFAYOGlxjYmQ9Q9sDM6Wph9DyFwamRMIKoDWEOY74NINubgWEYued//rrrxmdnMGYQABmqKe0Noe4x+lAYBYRGtqve6ueX1MQmMa9PbUVoQH/Iuy5VaXqaIYIM6ytyoSbQxyHoZm1og1pINdCfQ+zH3/8CYfTib7hE9tARoCqhkcUWx4SIWq/KVqT0AH00rcLmXdZctiySJWqh2mRrWeS5V0whKkHPVR8wCyDaYTGsdZEjwYDWZj5PB+fvZj87FWMThnHzXltIy/4futGXA6Fru6ggRxcfgpOlqjm9bOO2/q3OWOFEDAmKQ2/bvD3y89F86s4j4mBqQ80facN4algmie7epo/WDkv1tJeh3iRrSvU6gev18u7H37KYX36heXnTstqmHGs6gbbD1WF6DrhirfXRfzsnh0/M+vmC9D8KotednPPS/M5LjbJ9u4pCowem8K/tVyWfvoJuTsqyFn1Jk6XE6fLidDM+2F99tes935llpl2u3noP49TVXqoRoaahi4TjxibzA33PMbMLcFt7u69EKnno+oGXZym8SgUJejBUQIPK2GuVjw+e5Epx0jJ5ITYJMipo8qnv5LjR8dxypRL6Op2cOzwkawvNN/yfPM1l9/6AN+WbOIrd2zY1xxqFcZLV0Pi2ZH3qzjo2/9w/njbfTx7/+1m9cHQ1H/2RR4STGj/buYKl4uA5AbsMdS6j5qSc35MopmmMj97FaOTMkwv4LrwWJJvN+QTn5xWyx4aT653Nbk/FuJyu7n3pfkcdeJ4NuSshl+6w+BYM14iZzWp6ekB+YVZ2Mby8m7aVcrx/bvb2nxLGgfNW5oWwpTshGa6aSvj2PXWX9GqysImXnt3/IwQNXMpt9cjJSYxlafeWswf1wTdK++98gyJGeMYGpuEK5ANChF0MhnCXJYfmZBC32FxdoC8TzNwOx0IIZi5NI9VeyOPBb27mnKbfeW+qDKQFRTbZjClJNaqTnDlI9Sb/M3eMkqq1ICn3PpMcHIcXglUWHO+qEMayLVQnwd52cI3I253OBxhXiPrpmnre/y/gbRI1dUFmm6gakHDzB+40u0L2P43OKMzhAgWCPHXvRyiG01/eLUU1s0am5jCiLHJlPs0yAtPVD+gK+xr2ZoIgJn9RAG6hXigKlSdPt3ctiEohBWtHBz8Nd2wH/7W0mqFquNxOnA0ogwyWCanQkH2atvTKWorH9tKuFxu04NsqPay+AlxSbaXM7wEsvmfld3F5/fjdru57vb7KT1UTOa4E0lMbVh6ulKfFkgrZEmJiFgGFyA/vwDV7w8E0gg25Xk5fkwyjsBSuILC1vV5fFvgpZdairLqbQxDx9DhjKmXsOPnH9mwdqUtGxCGgaZqFO8/wL3//r8a7TX0V9y6Po/jR8bBlk32tjPun8sHP+sM+2Yfk2OPCPe4Bjw51sqEIFCwICEFVRPk5q6tu8HKEmY9cDtCCCpLDhKTkslvuvxE0caNnHbTvzntvEsZuj6PrzaEf21Ar64cFCqqEnl8c7o9nDL5ArYUeG1NZqjEwgrSs40+RSF3bTbb8rNJyhzHYV2zGNqvux3Zb4R4oBsrQKpSdawUs6MTUjhuTCJqLXrPh66/iH83sPpbGBs+hQgVOA3DgICGfEt+NidPGE9y5jg8z0zFf91cxLjLODIGe9IMCpV+DdXuH0GpT+OwrmaAqOWiMI+9aU8W09gWxKdnha02qBCxiExLY+z7uca2XT//gKD1V1UbikAQm5QCa4JBnPNffJKFLzzGlbfdz2/Ov8w2Dh2B+88RMAJ9YRKCYHaKzYW5tRrHAAs27OT4/j3oLaLPNDNzhhvgUIJGse01DsqkLK1xcEIbskod0if25MIQtmc62oi+XyFK+OF/Da0lFM6N9zwS5jUSgaug1KdxWBdXo42dpnKw0pz1FlXL4LB1fQHJqam4nKE5VM0bWFEUe3C0DF2BeXHHJKZy/Z3389Q9t6GL2hdcB/Tw2Jqjtqbcp4UZDlawly+CzrdHt27sq6qssb05OF1u/nTNHxCKQvcwD3LQU2bNllVd0NUVHDDW7ygJ8zALFP63v5wje3VlcJ9ujToOQ5gFLGJTMnG53WgaOJ0KrW0iO5xOjh8dx1nnXcIxw0ez+NVn+UHtjpVJPGxwFMHfxu0wJ2QrVqzA5/NhGAaqEBw6eIBLr7uRHp7GDVOh6eGqR6GHUlFVFWbc9u7TzzSOHWYukm3r87jr2vPR/CoOp5kuD8Vcmj/t9xfg1w0253ttnaLD4cDldjNuwoSI7TV0BenB6y7k+qeD1RgBeg04En7ejk83qFQNenRRAsubQTMpzIsT2Lh5XS73/eUPcM1rdbZp6DovPXgHCMysE4Cu6cz53M0JI2P46sMFcGy1QhAuD/96cR5z839iU4R9XnjF1cQkpuB2KigOherDxvYf/oeIOdLWF28uzOXf15oe/bkvuHn27fcgxZLkmPg0HSFcYZPL+ij3aWzdU4YhBEvmvsFnH75P5mm/ZeKUSyJ+XvX7+OqDd+GYmoUv6uTHgogGMorDTgdmrYTEJ6fxyKvzuDHHvP4W7OrOmYFrVgiz2I5fN0KkQiaGMPPoOp00qg+qY+lIYxJMOcD6nDV07dmbZw7poLScvKQ2XG43qirCJkxHHjM07HwOVPgDQYmmwWVNEFoT3RAcqlLp190DROjfv85Hf/16Xn/4/zj6hNH0O2EMR/YygxRtp5Ki4NMF5X4dt8NcjTYCUoMNIRUUI7H6h2Jey/2Z204Z1lqn2CTMYDszZaqhC1taEjbeQJhkynwOVVs5DcMcvUqqNCr9Ol3qcUq2B9F3RFGA1+vl4YcerP0D826PuHnoyFjOvuCysG3WxfHdvnL2lbeVEjZomBdWS5uzqTAHHSjz6cEgEKw8hMK+4CtVnW835LP4lafZWmgGbxwqLkYYgrqqbW0vqUIzqtdNaxu2hgR6WYb9/nI/094qqPFZf1XLuY8dDicZp5zJyws+4sTx41CUcA+ysGbWIZ2ihXiHCPk3aOQEo/obQ/7PB+3fcFh8Mne88A6nTb2YlBMn1fhsH6OOwK0mcOZ5l/DQWx8x+eIr+OW/W1mzbCk7vi2y37eNOWFNFARVqm73g+bpaUszDMOgV+++NaKiG4I1OBcV5vLPKyLnYAVw9+iNEkhLpzgclB48EAwwdEBR7hrbA28YOhN+dz4XXn8zD766gNEJqTgdpl5QURScLhdnXXAZ1/z9VlZ9/XWzcpurIyfwzYb/Z++s46O4vvf/vjO7m+BOBVqgLRYgnkCghdSdtniNOnX39lN3d6NODWupOy0USYgH10KN4gkkRHZ2Zr5/3LG1JEjl9/r16Ysm2Z2Znbl75dxznvOcsrDXpi1c7zxbUNedcRuuiey+Znt0FhbMJxRqkEEsHxZpJNvJa26SX5DZn0/nx0+mRJ1mmtAnNZMzTj4m+ppPDmfKcw+xvLw4rubrstIC6zpyhS1bMM/D7dYozpsXNj500ySku5vMpnQL0zQd43jG+2/x6O3XUZY3m5fuvYnvpr8bMxpmmibffbgbqjdx7keoKkeOOIsHXp9GSma2JQUpSMlwoyK6YTpJiPaz2UaVfYv2ht/5rq33IhNcG8NvFbUs21jtULz6pWeROnAILz54O4b296xRt7z4AadffhOX3vmo81rH/Q8I+z7Wb69jZzCEYcqQ/c4YyiWmufcSvAzDZHN1PT9v3elo9Macezr1wDAMXlm4nWs+WWJtRsO/E8MwmTBtIbd/Ws6MN55nibWG9sts3EO+vVbbI4rdXwXD82yatXnzOiNcidfwfovp9ltwf3eipyZcMHUhD8xc/Xc/UqP4z0COgVmzZhEK1sd9X90YO+ElFNLC5kg7DOZk7cc4RzfMmEoGewq745a2Dw9N90zJxjRMlpUV8eFrz7GirAjTNNEsoW7bmCsvLuDhy05n6ouPcc+EsZQWFZCSPRif309DYb0PStfz0vxfYw7w4t8q/1K9QzfcbA1OE+b9si3msXW1NXvvgwX0Sk4lNTPb/pNmPo+BjEuzsScWqUvrBoq9hrGB6YjO7wocw9Nq/JAVBfjx02nkf/9V1PHBvexBzzjmNIQiWFZayPsvPWl9iNvOS0uLHG+D3T/kPcp2KCtzNzJCUdheuc35TqvrQ5SvbzibvbJWkxXkrHYsXyAN3Hho3r4zfivbXFEUWrWzPMhIA2ZA9mB8tuapz89hJ41i5EVXkZQqpZ2WFedhhKSxaug6FVs28dpTj/DQfXfvUQEg86grSDgoNew1TXc3F6YpC38EQ7o7r0S0q12Nrn9mDr5AIzJcQkX1+RylGu/gNQ2DBTO/lFUIIxAM1rNiYTFlH7/OXf2jO2sopLG4cD6LCudj6NFGerOAD83O/jdx5hdbfSVt0JAwz5RXNxnPhtMuOBIP9mL8w1efuS8qKj8uKOGmz5fGPMc0DFlAxdtMXz4e9zNODhYzsGcX+G1R1HsiWEvu8FH0Tc2yNLKtWwjbOJiOIQGu0WGP6c3VQdZuq3HnN9z5zpvg2hTU2v3GNlpMmUCraxp7u5JiQxhx4VUcN9p1KMXerID3u47E0o3VrNm653O5bpis2FzNH9vrnHbZVF1P7osxxrBQ8PsDbFTaAnDfdyupqNVYUlrI5InPsLS00Lnf9bWCaS8+xp0XjWFhSQEH9E1r9F7k0vXvspBtDzLWfBPUvYSuiPXL7sO4hrN3U+tdo72e5WW7uNH7O/CfgRwDubm5+BugQvxv4nQyc4+Ti4kHrdu2jwoleLM7Y2H1lp0s+nPH3rhtB3WaXIxqNYPiCPvwwL4DWF5exEOXjuOD5x/l3ovHsqJcJql4qRFLCvMIDjwdY+AYQiGN4ry59E7J5NaXJtOxc+yqSTbyf94UM6TyV1Mv3A2rXf6SML1RL3x7K5zz8X0IBK3btneMfyEEzQLu9d3JQ3rq7Wpo9g68xlOB0TEAdrGZdEPSeGxDfHFJIdNffZY5X0wnFNSie2DVZmqq9m6/65WSwcryIq47+zQ2b7CIFZ5ow10Xj2VhSYEzWXq9ZouKC/ls6vvOsT6fj8SWbZn8yjMsKpYJZsGQYZWh1mJutH6tqJXcN6ufpQwcjOoPxL3f2mCICbfcJxU3DINXH76DFeVFTrGXvqlZPPTGdM644ibOvfEelhXns7K8CEUI/KogY9ChqNZGyDRN5v/wDcH6ur1SAGjG77H7rV1oI2TKzYXhbDBcg8k2ekxT6rZe+9jEBj9LqCqnnH0xT7wzg179UqPe31GxNfokoN3mZdwzYQwfvvwED15+RtT7Pp+sMNa2ffvwN6bKCFzzXtlWkrC8/6S0LO58ZQqjL71BSubZ2f/WM9n9xaWGyT5Q9sd2fquMvdnzbsgOP+5k942h57Em+Ux+rWwgkvTqBWF/KqqPwb9/HvPQfds2p+jbj2Ha7eFvfPYIxq8L+enz6QghjeJ9WibSvnkgory5cOYsZ4OPG4EEubmWfE57U206Ie1dhbNpt9onZeAQVL8fIjWx/yLM/cJuD/e1MK+j5zWjgWesCYbYGdzzBOyyP7ZTE9SdzZRhwtptsfvUoGNO5pYXP3D+Xrqxmpc+nsnt541g0rMPc/N5I/lpgZvzYkdlivLmWhUuG4bXyPy3QCCcezetzWqUZxjXCPZqeHvlKO2fcgzbBvTf/DC7gP8M5BjIycnh/nvvifu+EHBI/9QoUnnLtu0cuoINr3EUCyFj7xmNpmmysz7Ekg1Vca8ZMkyWFOYR0jSMhJZoA45jSbFMQnAmKBP2G5ADmadBzun4fH76ZeVgmCaHDEinwz77NXgfwfq6mJ//Vw+EyF2pTPyJfWzCzs175TOVdSWOcVVYkA9I4yrR40FeXlYU5u1yVEOse1660a1Q5BgC1iSzdafG+u2N00E2VNWxcnM1pikLbFx11mlMfuExfvp0quUSdYd6py1L4N3r98rze7F6UYnTtxx4PFKhkEFp/lxnUbd57oYhdattvVwhBNlDj+Stx+5i0rOPcM3Zp7GwuADDhGWbqli1pZptNbFDwc6CCuzTKwXt8mhqgA0RSKRozg9OxauQprGkSHqMFCFQBPRLy6J1m/a8/vDtTH3hUf533gi+mf4OPkUhOSOLY0ec7rgEvXxKn8/3lxQA2vj7L/IXUy48muHxJoYtQDKvoFrTKdU7NXhN0zT56M0XyJv5DT+vWBz/wEXfOL+Kb5/l4O1L3DLSERG3I0acxW1Pv4YiBM/eG0FJq/gTgDkbDaYv/NMaB/Lme6Zk0jcjh/KC+Y6mvP089u/u+DEp/X07hmmGzbnRzyePPfWMc7n+/icYMGgoHTOPjn3wKtdj6A+Ec17Pv+wqknoeHPO0V++/BV2PYaitkhQ3YUcmBCT6FRL9ShT1xKt/7jgqSguZ9OJTLCwuCPPS6YanHXbHQLaNFOR8c1D/dO6eOA31b+KB/vTZNFaWF4Xx8nVr/EiamPtQ8dYyO6KwN+CN5IFsn3hNkT70aHomh+tkl86d5cwj9W0P4PmV7oOJjFPw+fwkZeQ0jRYUdif/Djg6yLhrFHjmHc9my6Ga4NoT3qp59ubM9Sr/u57Vi/8M5DgY0C8p7nv3XzyGlm3cEqpemKbU+LMrPXn5YpHYWR/aq4P814pah29nmsTcra7ZspO+GTJ5ixOuwxx6Pu365jgd1UCqJzy5wn2u216eTK+UTIIhg/99vYIVm3dGXdeLQEKi4+XxwrvQRaImGGJTVXxaS1MQ5YUlvgf5j5LZe/RZzmdiYpoGmqY5fG9FCP742aXh3GOF17wcXNNOgjRdYxhMQobpyV6Xr6/fUetEBRq8F+v4sgXzHQ5pKKRhoMABA5zjNpf/BLWV8N1ze6UNbDx4yThat20fHlnxGo2BBNIGHmolGpmEPBuZ1IGDneImgYRE2nXsLDdxFh+1KG+u84ymCfW6EUZNKv6t0qGv2OPtzx0N96d1a1aTP9NDPTFNFvzwNd9Of5eVC4uY/tpzfDllEq88aEmUIavBPXP3zSwpLUQIOOa0MSQkJITNA0IIzjvvvCid872xDnTq2g1wN7OarocZTtheG1N6HKeUrue7lVsavuiGVQD89O3nYUU9Ou4XIbn13QuwUvbxfmmZ5J48yqGgqBHRtG69+9GrfwpLiiI2TITt1fi1otbptyEDVpQV8eAl43jn2Ue4+qzTWOQ1DJG/OMlrJk6+g+PV8qBW0/m10r6+fO3ksePpev4jbDGiE1+VtUVcmNyWI0eexfFjxnPv69Od954c3peuzQWTHr8rTiPG/nLt5LyjThlDwBdeYnpJaSFKDEqDacqk3bKSYm6/YDSvPvkQl55+KgutMuOmCXUhj0qC9bMmGGrSPGFzme3zajWdupBBjwHpJCTsXlW8XcL71xPSgsz6dFrYuFlclM/SMlkaff78PF577knKiwsc4ysSZX9s3ytjCmzjz41MSCm92Hhx/i98vyp8THlXcaV15/D3hl3AnROn0Cslk6Yavv82r6ogPNnd6+BxDWes+cduj/B1rM5R9rDamYZtgn8D/lOxiIOVy5fFfS8UDPLjxx9AhHHrco5tSSL5gh0WjMSyTVVSdHsv9JDN1fVU1GqenVrsXLpn567jwoHduOXFD3hhUQ1bgX0P6o2JNMwCQlBZG76gHTIgg+WlhSwpzucXX+NJBoFAQtyOX6cZqEJEqXlsqKpnW02Qzq12f4L2MnqXlhWysGA+2sEx7veNSzB7RZc03R0oQsFQsDiThwJyt71u+WJQ+gOSi1mSN5deltfBzu51OcM4tIj5c3/ioJSBVtU10zl+yYaqRmXz7O89NduVbjINE/PwCeHZ9YaOUBTaUcu2D++CkfGjJU3Gh3cSCmmsXR6hBeYJ2d7y3Dv0S8+iPqRTH9KdSmpB3ZCao+/NoCRvLpk5h1JVH+LbGVMIhWTbpg8aYm0iZL/5c0cdW3dqJO9vlbHGdMaSPXFL4/fA2Pe77Xf4/qXwWzUMVi0qZdWiUlSfH9MwEIpAj+DPGrrONzOmkJyRzYD0bB56YzozP5nGtzM+QNd1AoEA48ePj/mx3VsK1lXv/oDP3+bjqO119OjQAkwTTYdEn+w7CwvnkzFoCAMyJHVhcUkhyxZtBLUBSlT+ZCj8CIDcY09i+tuvomlBfH4/Iy68kpI5P7J960a6dDuIOV9/gm71yU77H4gADh8+mpBuoiqCb+1rrlnApC8eoVvXLqQNGiIVCyylD1VVueTGO3ihUh5qL6AmsG1nkDsXCcRBOZjLfkTTZGRhQEa2h9co5xBfgsDWaAdZerrsj+1hY2TrzqAl+Wc7KaQE11fLY0ePUjKzOfqENAzTpHWiDy1kcODaxfxaWUfnlokUFEcb+5HY98AebPD8Pfqym+idlES/tCz8imKNeygqyOfKM0/DOP81SGxFfV0NttRdrabz0A+raW3UuR56LUhx/lwOseYQh7tvzSFbqutZV1GDX1VI2b9N3PtzaBqmywutD8nS6Ka9aPyVQku65mzIvvvwPXr1SwZ6A7CkcD63TryR6+96kKfvvd2Sewzw+NvTSc+KVn7wJn3tKSKjFCayT8fDjLLfCG8oK7lXVTl65Jl8E7E375ksC/zc+UkpTWngf6o4SkOotxPzLNqEl9qlCuGhWMixZqtcGKZMrvXCNE3HPvk3PquN/wzkOFi0sAw4KO77a5aUE2kCOp0Dd9DafEDDjB4WpulKpewpfqmosSrceMJwcS78a0UN2xK6sFWRU/k9363i1iMPJmW/NgRUQTDCsp4+u5gvbxsnF4drZjThbqI5VLbnY+Xmajq3TOCAdtEenD1tB9OUhsJXH03my+nvo4d0RNJQOOZa96C6apSqjaAoTdf7rN8JCS1ivqXrIYRQGHXOBAaku2VSe/dL5qtl8hN8PivhyJSJIBur6hl2cEfqQgZ+VaAbJouKC7jm7NPQgho+v5+H35xO5sBBznMZSK9rQzKB9vfeNy2Lx9/+kHlz5lC5fQdfiz5hxwmfH78/wKgJ1/DaB9P3iu6p8tsifH6/1Kn1fvkeD1mPpFRs74GOO15smlG/tCz6pWWiCoWtNfXc9vJkVpUuYPChhzEgIxtNN8MqqXknVlvazg5Pfz75bSZPnAhnPBH7hr97HnZsjPs8ekgaQgIFVVUtD7KLVUsW8ukHb1OxbSv9swZz3X2PcdKocRTM+paRp54Ss0pmwYJ81j9+OpzzMjRr3UBrxkd1UOee71by5thUDIvkVVq0gP9dMBotqPFewM+Tkz5i606NeyeMJnTY+ZB6QtzrNaveSIcDDuCUsydwxjnnM+yYE8ibO4fmrdvy6sN3ENI0fAE/E265lz7pA5n4xlsYh+Qw64U7+KluB6Zh4PP7Of/me1EeHoeh1YFpYCgKy8qLOOLmO3n+g08p/e5jAI48ZbT07C8IWe2LY+jNL5ZJmmbWCJSVPzmJehDDU2xiFUexnQ/C8VJ5q+B5ebYQO6pmo127dgCsKi/m5/IF9M3I4f7jUqiq1xAC+mfmMF1VGxwvp557KW/XK9Rq8qhTzr8Co2obqqKgKtClTTPaNfeTP3cOmhaEkNw41NfWOGtEtcWp3aG0IBDwE7KK5aQNPNRjlMDCkgLKF8wna/BhBLKzHa9wMGQQ8MX2fy5cL/MO7LZcVlZESd5c+mXm0K1fmjQKrSZSv3oc1uSjXzE95rV2C57wgWEYvHj/rXCV3KCZQhDSNH748jPqg0EMXUcjSOmC+aTFNJDtKIrB75W1uyyHGQln7cRem+PPtTurd0CgJShWXxMCIQRHnnYGnfbrAusirm2aLC8r4uedjRvHpvO/fwf+2F5LRY1GnabjVxTZRg7Fwo1GGDZlTkQ/Q8hau7xtLAUK9H81xeI/AzkOBmWk8doP0VnzSn0VplAwrYXfWzo1MnQlEB41guhOYHsS98YOSiakuZ8vnQGxr2sAHy3aEPbax4s2krxfG+ldiDjv4z8UhBbCbGoCh72b9Hj85D1KwzneIrWnrbCopJCrzzqVYH29M+hERPb9kd1awKmn82ubPqxq6oW/ex5Oujnu26ZpMOX1Fxl2zAkM7nEsioBefZNgmeRz3vHKFPqlZRHUDe76ZiUAQw/qQH3IwKeqmJiULnArWoVCUF4wn/RsSwPWlN9Z6R/b6b9fKxJ8sXVKvaHk3ilZzA/uw9el66OOSzniFEZfezE9B2RQ17kPk9ZFHFC1GVrF4K3mT4bfl8Co+6LeGnH+ZSQPOYp2zX18N2Oy4zH0Gsh2cRTvJs72entDbwgI6iY9kzPol5ZFh+Z+93irO5nIjYVdkMY+z7QuOPfbL2KHUDyN5QsEOO3si/hl5VJAUDDnh7BDFEXBHwhw8S33sWzJQn6YMdkxnJeWl7C0vAShKPh8Po4beTrDR53OGedfzICe3WN+5NyfZhOqr4XKP3fbQAYcaTwTuVBFVkMrXTCPdet+lcoTjagS1NbUsOHXdXTv2QdFCJIzsunRP513XnrapbgETT544Qk6dTkA84/l8PRpcoGzrhEKQXVlBRfedBevP3w7hgF+f4B+aVK1ITk9mwkjZARj+tc/cOM5I+AyWYhjh6VUsrC4kBdffQsOOxeE4OgRZ3DSqHH0T8+yqBSWtxA38mJX97JhEh1psQ1oO4ExsnCSF6oQrCov4t4Jo9FDGqrfz92vTqN7vzSEkNXRjjh1LN8WzZD5GTYKpAGp+vx079mHt/unseDXCn6pkEleQoBPkevBfq0TATjh6CN58pGHqLP6U0Kz5tYTCLx06jtfmcK68gJyDjuM3ilZ1FkUmBVlhdx78Vi0oMa7Lz7BC+9/zICMbLnZjihf74XtpTZNk8Ulhdx2/ihC1qa8/1m3UNUm3Tn2irEn06Hb9dwZLcyx+1DC5y7vxlOoPnx+P8OOP5mywnxCyEhGSvbguEaUvTnaUFW3RwZymBfUWqfi0fPkfevg99xT9ijUX0vp0bc/bzz/MJwUzrs3gVmfxdASb+B+/i34c0edFZ2T2s7rd9TSq1NLdyxioiKw9oSO80IVdvU9d1Pn9ToHdYPKuhB3fBdLRf3fgf84yHFw+tExQvDrlzN000yHK+kPBDhxrBtKNcEqvSj/doS0adg7aiIF8PcEXo+1/S+eITp/bUXMewBpHOsxTvMlJMhCCTFwetr+Me/Hexlb97ehYW+akg+3OwjpBiX5c9GCmmscC4Hq8Vg8elJfDt8H5nw+ndWLSpt8bcUXgLcub/AYwzAcDrJcEN3JtWdyhhNGtmFPLnZ0IcWiRdiSYv2zcpwTvHI5kaGqsHswXb57yDD4IIZxDLB/l/1ZWpTH9x+9y6+rImSu1pXA+zfG+QQBv5ZDxR9R7/RKyaB3aiZZA3M4fpRH0cCzqfLKdBX9Vslzc9eyoryIj157jsUlhU6CnYnJ4uJCPnnjeVaUFTkGgz3Jul5B+V9VXSh8Y2iaDD7mxAYH3Wmnj+f+16dzwQ13cuE1N1NeGC7nJBSFzCHDeP69jznljHO45I5HuOCW+6OUa2QVviCfT57EVWedxrKFpQTU2AvrYUOHoapqWPnt3YGJYF5BkbOQJ2XkOFxgv99KBnJvsOGLCUEopLGoaL7Dj11WVsiW9b87z2oaBiV5s/nuo/ejwmBCCKe/HjPqLO569UPGXXYTt740mT4DUmVymuecBfPmoHmk97Zv3YIJTCpZj37Yuc7rHfbrSt80NyITMmzevuloBTuyjjaxynQNi5BuOJxkr9NCb2DTtLVGY/Zn0wlpQRnZCEoNaGnaSg/h4cNHE1jwAeRZiit5H8DcSdbnGCwpykMIyDqgLacN2FcqNeAm6NkYMmQwM774mlZt2gKQkNgcwxq3P3j4rQcPyGDcJVczID1bPrX1jIsL8zybIqkZHRa1jIHtta4MqUmk7nSQEo9xDFC1vYLeqZlx22t30Td9YMyqOanZQ3jg9WmcMu4cXnh/BhdffxtPvzODvqlZmMiKmF7nTaR2/J7AmT9Md25uVM0/4kNDox4g/7svCQWjE4iLi4qYFUNLPBZsqua/BQ69yYQHZq7k9q9WYJiw01L9sEui12ohNF0P5yKbUqLSdnBoeric7MJNdWyt+XurvO4K/vMgx0HzGNW7RG0lR488lSNOOIUVpfkcnjuMHv3T+fxlV73A9m7Y8O6aIuEV1l78Z+Mc04Zgejqx5YxjWUSxARs1sYxQ29NCdLILwM3Pv8/q0jwmx7heVGKI9cw2ttUEWbu1BukhjJ4YN1bVs8MycpY2gWsbiWDIYNGfO5xy2Jomi3cccepYOh06ivesxH9FCJZaPEJzF/Q+u/Xux7rlsxuctISikpEjw8GtE/1hBvLysmJmLimgf5a76bLb2l7o+6Vl8dQ7M5gzezY90wbSOyXTNQBMW9pJUK8bJBqx+XHeDUis6oE2vp4xDXPuO7INDkwJ9wh/dDe0aBfzvNYdOtMlfSCrAwlRVfkeu+Fi7po4lW7DDuP408by2eRJUtXBq2JhJyYCD/8gReHzyr7CnDuJ6ROf4slJM+iXlsmikiLuv2QsIU3j49ck3SQnZ7DlNXRa3LnWis1VlpkklxYDOG70WXz5wxx+jtMGJ446nU4tZXKd9MCGL2o+n4+LrrmZARnZzqatentF1MolLG+caZoEg0HWryinXfNxMT/zqNzDGHPGeN6rinNTu4DnVyqYZjGHDcygZ0omD7w2jSVFeaTnDOHApDQOqw/x49J1GOnDG76QZeAmZ8sCNwuLCrjxnJEE64PhY8SUWs+Rhs3Aw4/j5HMuoU9qJropvax90zIJ6gZK7Xb7I9zjhxyGPxDApmi2ad9Rtl/b/cFWHRQKfdIGuYatkIvwivIiFhXmkTJwMNkW/chLXZN5H4LlG6vYGdSd1xxj2mzYM1fyx3aOjtwAeH4KAX1TMrnr1ak8/+UC/rTuVf5QZDtmDXbPEdK0RghUJVq1YuCgQQS+/wbqIVhXg26YfLpkA28W/u4cIxNaXWPD3iAmZcpNUUhzOfqLigsoXTCP9EFDyDowuqLfqi3VTqTFME2ZrG1dQyCIXBUOTh3IqoVFRO2K9hAHHNSLHRXb+GNteAwvKS2Tvin7Y2LSPz2b5Ixs6kOGY4it2bKT9s0D9OjQ3GobT9R2D+/Ju3FwnEwNLRGKAjHINgtrm8OIaIWgx5cr0LphedSw+/kXWch2n7n1y+X8bikq6YaBZghUq0pgraGjmzJK6FOsSnumwBQy6V+1OO71IZNmAdOxUZpaXfSfwn8e5F1A5/26UPjjN0x/5UnatmtPckY2vkiPkmew2uLa9oDzYr0lSG6fsychlby8PN564UlLCkiaSYZpsqS0qNFzbdi75V+21XLZh9ExtR79UlnbIh4nW5DZtY37l3AzVEHuIG16RazH/K2yxvIu7p7knd2+tpF59pU3c/YN99Bxv65hXGgF6Jc12OHKRmJIx9gz4tqyBWESXg7mvOX+bhpOKdRenVqGZfU/cNk4Xn/qYW48163qdsa7pWyqrnf5W9b9n3rBlZbH2YyYtK172VrD2m3xhPHdcFYoxibHhmEYrvFTH0ORJM53sKNiK8tKFqDVR6tD6FqIpZb3bEBGNmdPuNK6ViTFwtW/BDBST8RMPh5N0yhbMBcTKMmb64T3QyGNsgXz3I2CGb6Yue0nX1tUXMj7Lz/D0tIiOluKD7GwdsVSFAEBVVBdtZ2wJTaib9gSXf2yBhOwCouoPh9nX3IVw08/B39ARlcCgUBM7rEXqWmpMV9Pb7frTPAXVgkWl0oN8y5JqYy66CqS0rLQTStqcXy8SIAHQuHi/z1I/7QsFAElFtUn7gYyqm+Y9EnLQiBYWV7Ea/ffwsT7b2HVwmL8PiBCDPOY3MN4/r0Zzt+t2rXHMKFVa3f+6LhfFw5JSQ+LOCwtLeKeG69hauEa7r5wDEtLCp1xI2c80+kL1cFQWAKXdyzZ3OB4GHbyKFkwRQj8gQCHDx/tFvZAbogUARuXWnNr1RYnMevOV6bQNy0ToQhWLyzmkzeeZ1V5MYoi7y9yyikpXMDWTZLqtvH3X1hSUkhFRIK0aZqOYoUc1zLJrmdyBne9MoVzrrqFJyZ9hAFcceZpvPzEg1x+xmnMnD0n5lfnXXMOHpDOXROnMvrSGzjhrAlRx3frl8bSot2vCBkP305/h/W/rHFfKPoYsCJMtpyY7YF0bt4Ny7vPE+14ysvL46GHHtrlIj3unCQ/2C60Ew+BxGaxrfL0U+J/yDnPx3x5fPdoh5UZdk//LGxv7+8euVFbotMeo7rlFNN1V6HCPtc+xvn+PPN1pFTuvw3/eZB3ARvX/8HHn74AwML82bROVMMoFuB6jAEn3G3vlrxYv6PWmtRFo9SDhpCXl8eRRx5JfX2Qt55/gqff+Yh+6VkYQIs2bSGaRh0T9uT5yZINMe/lgqnlQOwdsInJTYcfwk2fL2WdxbuzPdI2vGHxSNj8acMzcHYV9oTSPz2LyjqNuy8cg6YmIg6thv5S81QzDPqkZnLnxClMLVpHmN7C06dRPe4a2HdY+IW/eQbWFkZ/YP4UJ/sf67NXly2A448AwqtkaSEdDJ1QhNt13bZaerRv4Xi3FFzj0Zv8YPcOu31iefiFVWjAfufnhqpLeVfqjavh04dguM2ZE7Rq2ZKYTs7V+fbTRr3l8/vonz3YenZ468Wn2Ld1Ao8+9axzzJqli0jaJwZ1SfWhqiopA2UiY9qgIRjXzICVc/F99yxJVnlWWxkmcqE3rHtaWFTIVWeeSkgLovr9ZJ5zS9wmmP/DNwzOTueLKe/wzsvPhr9pmui6TskCqaAgEPgUQd/UTJ6cNIOFBfPIGnwoKZnZhHQ4ZfTp5M+bw7kjTqBbt/hGeV5eHnfcciMcH31f1ZvXg69rjLMaxuLSAvqnZUgvoykpON72aRSKQnVlBT5F0LZZgIxBQ/AHAgSD9ZaKh6wyKLPOjagL5//wNbM/eo+uh/ThjgtGOeHlHz+dwuOvvE2HzvuFdbfWiX5OPGoYzPvOeS1oqSjY8AUSLZqQNY8Cr5RtQzvrGQC0xd9RumA+fVKznPnVnltM040syHNdS8vEZPwHpQ02R5+UTO55fTq/lBfQO30QBw1Ip0bTEUI4dJElhXkYi76F6m3wc5HDi19anEdiQEXTDe69WEZAZvj93PXsa+yzz35RpsD8OT8BUhLMNE0WF+UhMkaEHVP+xw6G9GjvGPqG9eWaQK+UTDIH5uBXYMrEZ11pR4LM+PI7jhx2mHOd+pDuqFdg8UmXlxfxffEyZgWGQIdYrSHkmG64yXYNllZ0mMPhpzfwpx/P5LL1BHWDqw7rgTfx0stj9cJ6Euc4ey0MBoMEAgFmzpzZ6IbVey2vs8IwGk7olAkPMSKx7aLpho1hRXkxL59/AZd4HFP2/G8PC9M02Vaj0aFF/MJHfxXsceVF0DBoZppU1es086thto7h2DVuVC+yHLW7ufl3bALi4T8P8q4gwgXww1efhe2AElQFlxEn4UgMRVzKHoym5+/dwaxZswjaE6MWpCRfctGWlBQy6YUnm3ydVVt28nbh77u1n+vSOjGqIIc94YA3DBa7QpD77LvXCJFc78UF8yWN4pTbMfqHFwRQgD6pWewT6V00dH5bszz64ktmyvNUlWZa/FKYgUBCWGGIn5eHe+HtEGwYwrxcctIY924Jk8vWo+mSC26Y7vuRPOZI2H2q+LdKbv4ivkwhisIBB/V0+bSr8+Cty2g19XrApGrT7/DedeHnPDcWNsgEQ356I+ytzkYlNz0+kT4pmU6ZZiEEO3bsAMNNwin48Wuq60Mx+7rtYQDoY/Meex3KbS9PpndKZtj36918ufxs+PKjyWhBmaAZSjmJ/Fbx+ZPrVq1gRVkRX0x7N/pNKyO9bbt2LC4p4L2Xn2b1wmIUIMFnyTkJm5cKyRnZjL/smkYX41mzZkVROQAI1dN+n11fWAFa9soK21TVhwxM0+Snn2NXv4uEqvpkiWdF0KN9c1RFcMLIcZw8djxDTxzJvl27cfRJp3Hy2LPpMyANIaKXjPnff8HiwvmyVLEFXdNYWFSAAA7uEK4A44uYLOp1I4bCj12MQM6f61XXgvP5/AzIygnrM3bfiIxUeT2Rhtm4BxmkkXz+ldeRZHnGAadfKwL6Z+XIsfxzIWCiqCo/fjKFyS88xm3nj2LWp9PCIiAry4tARJaWhqHDhoWtKX0zcqLa4bHZP7vPaW0aNlbXMzH/F4vbKSlFadbGRlVVqXhhqeYAVNQEWfxnldtOmHw2ZRJ3XTCSWSs2EA82peSQurWNtlmTsGMzvCcVhSK5/IaVrPfRog1yfJvuWDe8Y997jmf+NE13LdyVSpamaVqFSdz1yfYeN+SsCWoaKHvHv7iirDDM8LUNSC/qQwZrtzVcf+CvRGRbhHSrRLwnyuMYvnbbmS61CSzD2TlGvvZvp1j850HeJYR/m+3bdwr7gtevW8XPiypp61EfCIYMFMWzE7bkTrze0l3y+EQgNzeXQCBAfTCIzx8gdaDM+C1bMB+toZTtGPh25WaOOCSmKyEuruqrMvigDiAEmsfjEyXzZntyYnqQba/GrlNNTM+CaGdoJ1mFUILtDwg7du3q1Sz6ZA4t27bjh68XwKHnhL2/bUN08hlI4/ii2x4i/bjBPDF7DSu37AyTKxp85PFcf+ONYQbS3G8/h07H2Rfg4D5JrD7m3vDntu7ftLJ9dUN2pi+WbeLsjK7UaTptEn1OG0kNVUF1fYg/d9Q5GfHetjBN+LOqscp7Cut/XUtW7jEU/PC19fC/U+UtS75xddgZJ44+g/pgkJatWvPzssV08v/KTO1ABndvx5WHplNfucXZbYdNeh5PkSkUTKAmKiFVYOg6386YSvmC+aQMdL3MaxMOIBnZa5aUFLKoQL6fke1yULHC63i9HAcNbLAF1q5axq3nj6JvclrUe0IIDMPg8btuAQR6KIRQBPsdeBAbfluLoRtMeuEJZs6cidqlj/UEjc/0ubm5+AMBdO+h2zegTr4Jbnur0fNj4d11CmrHzRzdS3J5dWSbTMz/tUnnDz//SvqlZ+NTFfLz8rhk3KloWhAhhKMw8Oeva52CF6rfJ+UePeN02HEns2+PXqh+v+NBVlSV1MxsANo1D/d6RRrIphlecnhDVT2Pf1nCXafK8yNnhJxjTvEstLbygMVJ9VzHMXhs71ZTphYhnKQ6xfYaWxshRci+0Sc1k/E33sPsT6bQvvO+tOvYie8+fM9K2DRB4HB7fT4/qZkDEQhHszviA8PuNxb1yymvjQxlvzBvHUs3VnNo9/YM6t4O1RT0s3TEy/LnkTpQamHbHkhvGywuKeDbGVP5+IO3MfzNoE3nqM+zsWphMakZ2fDTmxAxd+0OkkK/0DK5L+06DKVH3wG8/vAdaFYFRr2+FgJyI2XnXng9yDG/P9uQtuQd7bXQ9iA3pZKl6bRNeF+ChvM44sl+7g56pw2MUlT2Os+89/pPwFqew6B7gkmmEU6N1A3wKdZJnnu2n0k35RoggMQ4coT/FvxnIO8KIrwn333+ESPOPp/cfQWzNpisXrmCW1+4lCcnfUTq/kdhWh0h0adiAn9sr6OiVqPvPq0AnMHozcTeVeTk5DBz5kze+uhLsgcfyiEDMjCBAVk5+CZPjkqmagyFvzWRk2Hh5evOpPPzb4dlnNvJipW1Gp1UJWKSjrEA2AMNe7MQe6GIhT+217Gxqt6dRK2WPPTEkfzk8+FVr3312ccxl82WxR+ST3ReV6u3oCOgMtqbcsTIszjylDH0Tc1EN0yO7tWRlVt2ctihQ9hh/Erm4KGMv/RqukXoOldu2wq2UtqQs2jHb3Gf2/5ZE3QNR9sAcEKLtifFtJ+7NtpAtv5FeqqioCiO8RNISCQYrG909r3qjgcpLMiXoeOghlIRghNvlkUirM9zwtDgcABVn+ok/yzY/ziq60P4IlQehKqgqCpfTX8PQ9dllUdLe/Xtoj84ud9+LCou4KZzR6IFNfwBP8+++zHJGTKzX4bVTY4fOZbPp7/XtCRM00TTNLod0ptFxQVOMRBhhSNM07S8vVZOuQG//7zSOd32UB19Vh8WFhdQMH8O4rSGKRY5OTnM+PxrzvtsnVtMorYKs66KbRt3j2IBUgPdNHEqE+4ad1GgKtL4+3H2LDRN6s9GQvILdY449Qw679+F+p1VrF2+hEOPOZHh486hqj7Exbc9wEv33YKh6yiWhzBWVywuWOD8vqNiG5+++RnVnYfgXYXLKqWOcofBg6O65pyvPib/s3d5+I3pDMjIdhZhJ8xrzTHOfOB5vymwtcYdoxg3SW9FWRHffjyF72dMwdB1flu9nBPOuMChDJiGwSF9k8k9eTTlC+bTPzOHpP69EQL8EbWL5/z0E7CPfXOSw581Kup+vFU3v1+xmaUbZSTLRHrE/YrcePZPyyIlI9tJhg27BrCwuICrzzqN+nprvJ/5JLTdL247LCvOZ9PaVaxZuhCOcV8XpoEZI5LgYOkPkHRE1MtZw47hlIxLABnJy0pL5pUnH6Fw3mzMkBtZWVxaSFqm3OAayHlRrqHhPG5Z7c79rjOzBzJz5kxmzZpFbm7urtErPH+YpqRXNGgg70WcOu4sROSm0aEluH3RMGUl3K5tE+NKff5ViOxPXy7bxOiU/bFls71v12o6flWSX+qsiI1hR9INk6r6EMGQQYJPoX3ziKjqvwz/Gci7gB6dWrPW87euG5Tkz6OnYTJrcyfYrzfBxLaU5s/jjJOOskIQLqHdxCQ/L48ZS4vo1CfDree+Bx5kkAuv1rkXCT6FWk1m/Hbtk0aXi55iXXxWQExU1YcaP8gDLRhkaXE+/TwGMsjn+X17LX9YxH6vwH003AS9BupgxERdyPBkqsNnkyfx9N03o+/bB7qPDTvWMAxMQ0egICp+lz6Ksi8YN+gQpvj9hLZvgJfOgkvdsPv5tz5Es4CKAhhCMPDAtrRvEaBv5zSanzmcmorNqIog8rHatPd44tNOJuGPL6Kf2gk1ybOrPAayYYVOvd5xO2Rle9s37KhjX4+RbHtCGm3CGrkJ6tCxM+fddC+vPnirYxQpqspBfQdw1IgzeNnTd+bOn8+asgWOLJS5USbZZB7QRn5nAvyW4bu4pIArzjiNYDCIoqph2fFLN1QxIMKTlpQ5hJaJFSywvNlRNATTdCT8bK3fkvy5DMjIQmqJy++/f3oWT7wzg/y5c5iV0J31DaxvwufD7/dz4shx3HzFBG67+wHmzPxayoCFDcbYA9P2UG1bs5jLTj+VYDDI288/weTJkxk+PL5yxMBBg9i31GSDVbABIak3HfbZH5rGioiCaUpv3+qyBfRKy+HL7e2afO6nb7/IkV39ZGQNIjc3F5/qQ7O4x15DWQiB3+8nd/go+qRkkuBT8KsKiVb5ZJ8iqK6scJpL13XKigvIyg1XU8jLy2P4CcfCpVLyaknBXJZ+9SScIKDXkLBjFxfmMSRnMKGIBFlTQEjTKC+YT7/0bKcN7DlAMh/dogR2iL6hxFX3OaGZX/XI0wnnjeXlxdx+/ijq6+qxHzQUgiVF88Ku8fOyRRw98kx69EtHVQRCr4o5JocNGwa/WCXpOx/ELx260y3GjsJ9Lngp7xfnda/ElqPj62yS5PP/uUM6EEwTZww5/bsB4xigVZt2vPLgrVEJyqJoBqxegHn6o7FPzJ8a00D+7fffEBky0VsIQUrGQC6wJBbrddeVc+M5I3nqnRmkZQ5EC0m5Nd1j9Gu6wcL1O5yRaX/HC9fvICcnp8mGMYQ7ZWxDT1YmDDJ1wUr2toJHJK7uIzioQ4uwT/F+35GoqA3SJtFHQsu/1kDWdINlG6s9a2v43Xy+bBPN/CrD++3jjLnI+9dNN6nSjgDYbawbBn9sD/LB4oq/9Dn2FP9u//Y/iMgs2PGHqKSav4a5RFRVISNnCGmDhsjpuGUHzPMnkmzRHLwDWDdMyosKuPT0U7nzzju5/PThPHnHDZbyRLxleNfghovg3ZLfd9k43h34fSoDsqK5c7YetO0dbyhJzwmfWT+314XiFjmJhMDl7y4pLZTGcf9jYcyD0ceqKoqqovp8KL8thElXosx9k949e3H3q9PIyD0WJRjO87J1TC21JoQQ9NunlfTSWg8dy6jPPfbksL+zj4iuZub1cBmGyfJN7hf2yeINbhjZbjXPpGlYGxDvfdqTfYPInwJFH6EoCieNGkeVR7pMCMERp57Bw+9+wbGjzgo77eHLTqdV2/au3m5tJff0NxnSoz1CkQSDFgEfAsGSojyHCxjpjTSAz5eGV7DrnZZNq/YxCpNYWF5eRJ+MHEcn2i7r7Uy6pjuBJ6VlcdJ5l9Ohc8OSSl169OKK/z1AckY2OTk5HHGYpR0c0YBCCLod0ivstWHHnOAkABXnzSWoyRyA+vp6nnzyyQYz6FVFhHn4O+yzL3dOnEK7Tvs0eL8NYfu2LTx82elMfuEx7r/nDgp+q2zaiTWVGD8XUr5gnmdak8+vqiqnT7iKrMMOZ/wlVzPh+tt56p0Z9EnJdDjaPlX+FEDLBB/J2YOd/uHz+0nNyI7yINs8UefTDAMj+TiMCOMYICljkPTkR2p/KyqqqtI/a7DHsDGdseJdsL0KQjHlLWOgmV9BIAioikMHEULmNgTrg3hnayEEgUB4JKdiy2YrmiI83ufoSWJQTg7tOrj9fsFWhYJfow2GeFKh9oYYcOYGe64wMVmyoYr12+tkFUITRwZTUZq27Fdvr4iqIAmWo+HP5bLgTSzoMXj2wKzPP2JFuVT/aJEgDbzk9GyeeXcGCZ7qh5qmUZo/F4BaS8HDrqQIOJsC1+hyHSy7qvzgqEzZz2Zd94nZa1hZ9dcTZI8/LNvpIzbsCGzkozTGi96bqA8ZBHXd2pzGjnDbFXftstFO6WhDfl+1QR3NkJHkqroQffdp5YnmwAPfr2Lx5nBN+G01sfvOP4X/DOQ4iCT4b15aRL+UDAIJCY7M0033PUZq5kAGpGfRtZsrgZaUmkXBgnxeeOoxVi0swkTuyIrz56Fp0nDQgkE+nzKJK888jcUlBbs8sCNhTxL2bvrvwoljzpHJWZb3AtwB7r0PZ4K3NguR/Gh39w6rt1SzdRcHigmUF8xDxwdHXBz7GFMwMPcYjjxlrPSKbPkFU9dZWpwHAg7ul8rw8ZeEnbNqYbHLR/S8LuWepJEci3+aNCAl7O9ufQfEuGe5+JuYfL9qC/d954bwp5b/6X6XHiMwTCLN+rW6PsTPW2SYfXFpIQtmfRu/oZb+AIZOUno2/dOzZcKRbXgmJDD0pFHSAAIONjbKTP31y9FDGtXbK3jg9WmMu+xGbn95CkLAp2++IJOQkEZEywQfo048loCVMKSoKuqnDzgf/8fPq6MKmJhISoyTxJh6Ytj7SwrzOHhAOg+9MZ3xV93MQ29Mp396poeG4vUE4XAvG8Lva1fz/P23s7ikAICjjjwCX4xCOKqqcvsjz3Lj/U+Sddjh3PbQUzwy8V3HS2XzHhVFwTAM5syZw5FHHhnXSFYtGoqNFm070Cc1i9xd5P57UVYh0Nrsj2HoTqW/pkCZeC7+YA3pgw5FCJg9eza6Lku/GrpOq9ateertaVx1292cc/m1DEjPkpshITctk19+hqWlhY4h2C8tiwden8bpl9/EPa9OJSklLWrzaLeXg77D4o7XXsmZUdJeAKSfwqHDT6dvaobj/bOTq7wRGTOsX8S4TgxIY0WwsLiA9156mlULi5znS84ejBJBkzBNk1Ztwz327Tt1YkV5ETNef44VC4ud68b6rEg4KkARn6HbD+PB72tWMuON51lSWhiVsGaaENRtLWg51/RLz+LJSR8x4frbufyux+K2QdfmgpdHDQh/rp2u4e5fM98qGhV7kInqbWRUlUa9bhhWIRUgoHopOIL66h3OcaqqkjpwiDM/gisRaZ8TJvdohn/Xuwp7jrW3VQYmmyv3glh5EzB54jOsXFgUwZ2X/xb9uUMqvOCJhPwNa3tIN6jTbNUT+VpDyfVfLt/EWe+XMqnoN3bUhZzjQ4bMubry4yWOQof3O4qVI+WlGf4b8J+BHAeRBP8eSckIAUcOH8MxI8/klamfc/o559Onc0vpRfC05OKSQk498TiefPA+Hrr0dFaUFaGbJukDh+D3BxxvgmmaaFqQsgXz99yD7JkkDNOkiU6CPcaMN19g0lMPWAthtPHm9fDZPOTKWo2FVoi57I/tnlCoa+Q3BU4mreUFSM4ajD8xvgyOCeT/+A2VWzej+lSnal3rtu2456LRTH3hET5560XnePH8GJYV5zvap3aTKtbf9pwWyxjbr3UCnbyZyTEeyt44RXqDPSc5nhHbAJDnhSet7AzqVNQGWVRcwO0XjCLvm8/itoF9UreDe6MImbF//2vTOHLEGaQNzuWnz6dbE7bg/LTOBN6+BGXarU61tL6pWYy48CoEcP8lY5n24mPcddEYVi8td4whmxd/3333cfLoMzA8Rtuvq6OVQgxrIjUx4eCBUQZT34wcTFMWoRg34WqS0rIcQ8j2lmEZATssilCjPGxdI1hfz5cfTnHuefQZ46MOu/iKq0nOyOaUM8bz1NvTGHHmuWHfd05ODu/N+IKsQ4c5RnJDGfTSg+z+bSIjEPu3TmRss3hlTRqBEJhnPY0IJKIc0L/ppykK5910DwPSpQaybbzaSgjpg4Y4mz/F4xFdWV7E7eeP4o2nH+aas09jSUmhE13pm5rFyAuvJDVjoH1rYcjJyeGLr79p0v0ZpknIiJEslXYSwfQREREpqw+Y4WPDNHE03hujWFyfK50ci0sLufT0U3n1yYe44ZyR0kgG+qVnc/Xdj4QpMJiGQfuOnTzayQkc0ncA/7twNFNffIx7Joxh2cLSmP0xPz+fim2N82pMLHWSiNc/mPgcU198jFvPH8Xi0kLcjUE4nc02rmyt9bMvvYYjRp4Z9/Pat2lFu2Z+qior3C/w/RsQXz3BXf0N7njoScZdfhPtO8eIejwzAjApeyO6HL1QVJKzXClIkJcvWzAXbA6yaXD0qePo66ng5xiHuNFY97VoZYRdgXeTDZb3Uzepr/kbwq/ApGce4d4JY1lZXhx2U3bfDjci4z/jpqp6fq/cswqdNtZuq2Gdlddg/4tlzNpr2jcrNgPw7cotvFX4G+5GI2pPhzdHIjKq0qdzyz0qF/5X4D8DOQ4ieUymEDxy/QS+mf4esz6bhhBysfOrCktKCvl5kVuU49uPp7jSayGNpcV5mKYsoPD8ezMYfdZ5qD5ZrELqv8pElKq6XeP/RsIOL0ITDIS9BpOP336J5Z4BXr2jkqWlhe4ihTsR2QPGKQlrGGEDcVc84OXrd8jyqdYH9E3N4tbnYsh2ObdqYBoGC374GsMwOLjvAM658R7WLV/s8F5N04R578EXj+IX0C8jx5rMYyTueNo4cuJSFcF5Wa6KRkUMj7hhmpbyR+ykRBNPeWbCvSTe0HG9puNXFRYWzEczBLRrgFtoJa+1aNXKDQEL+PGTKSz44Wu+//Ad7rpgFCvKi+iTlsX/Xp7CyIuv545XptA3NdNZ2OyKhIaho9XXM++bz7D1VUGOn1tvvZUTRo7Dm6h8wCF9om6p8LftfPf1lzKc60+Ifdt4wqF2WN3eVHm8HIZhsujPKsrW74h5HQDx3XMyemCafDbtfcfbO2LcGag+Ny1DKApt2rSRv1v/yd/DkZ41kAnX3kxCQgKqVTAkXga9ahO27ecynZ5FbVVlzHOyfJviPosXB136LPrgs5t0rPxsk+rtlSiKfDZ7Y3PL/+7ihfdn0D89nCIhkAvGkogyx6UL5jqtY48Nv0/QPOCjZ8eWUZ+7dMmSJt3fyvJiQobhLMBe+Nt0jPJw2YtyZERBN2QZ3LcLoxNlbQxpvo0N37/HivIiyqxIn/18Sy2vpyJg+NjxHHHSaWFteEjfATz0lvTMvjT5E6q2V6LVW+cHg5QVF8TcRM+ePatJ7bCivIigblAfof6iH3sNZsZpaPX1fDtjqrwfHDM5bJNgbyCsd9AaSEBTFTkn9M/MIcGOmNZUcO6px9E7NYNeKRmMuvBKAgmJ0SfrIenx1mOsZYaGk8Vreekfue16lpaXIDwc5CNOHRPm+QdLTsyU0TKHG2vamyI3etBUGzkvL48HH3yQ2XPmhfWhoK6zUwvRokV0v/0rYNsIiwvnO6+1SvCBcPt2SLerCcZXedpQVc+GRtWLmnhPERtMkLk+kbCLpHm7tr2ZNQ3TKQ1v485vVoR5kCOjSyf0ia+o8k/hPwO5iSiZ/Z1rEGgaP5cVcEhHKfVSumAeZr1bmME0ZflP20PZJ2OQM4AHZGRzzGljwnlg1kSwYnMV1buYJOe5hCfcaFKxOb6+5V6HabK4cD4VW+RCXrW9kjsvGuPyq70GsOd+wesFCPcENMUb4Gqgmpa3yaCyTffYBy/6FpbPdv7UQyFWLSnn7cfuonJbxAK8YAoD2inc9vJkeqVkOEaxPbHbGTy2BFQsDrIQoHrUGu75blX0Qaa7qdm2cX3U27Zua6SXI8xgNk1qQjJreED2YDjhesg5PU6L4RDFPnzrZZaUFNLMr7K4MC9MwzakaSwumo9Aem2Hn3cFfVIzw7R/+2XmWGFWeQ8/ffUpS0qjC6qkZGZz3R2uN2m/7gdHHbN+Rx0l+x8Vdn9eLCnOczxizk/P716D2TTdMtbx0MPY5HjGDD3keHtzDxvCzfc9hs/nQ1EUEhMSGHzoUEvey3WmRW4+u7RJZNRxRzBz5kxuvPHGBgsUKEIwLtXdwAzu3s65bp+U9OgTpt/JvptKGnweG2vUJpayXfgNLP/JiQoIIZzy7jk5OVxzw02WNJlLH1KEvPcEn0r/LC8fPED6oEMBy7CyzvApggSfoE2z8Cz1iRMncsVllzbpNu+/ZCwffzCJ1xZES9bZtAPXW2o6G2V7G+7OCybfrNjCV8ujDW0b+bNn8sHzj3LXRWNo274dfr/0pPv9fvplDZbSbYpgSWkRs778xD1RCKq2V5Kakc34y64lOSObtu3bYauomIZBmzZtYxrIucNyG+cCAQ9eMo5lZUWUFxVEv9lT8rS/+egDFhVLL7JX+jKceud6lnc0sM7UVFfx8evPg4BH3vyQ8Vfdwr1vfMgRI8605jv5HR9klXxuEvInY5Z+7hiCS0oLuXjsyXz03pv89O2XmLZeuhAsLJjHktLCsOiAy8O2N8j2dxweQWoKVdEuKHLnnXdy0vHHSoojWLQUyadt3nLvybg1BCEEiqoyINvl4KuK5L/bj/JrZS1rt+10HSRxrrU3qZXuvCrb+52i36OOmf3zVj5evIENVW5lVUXYcq0WXXKrm9Pz5bJNzvUgmpq4qwn6fwf+M5DjIC8vD99bl8hKar8vYcGHr8qqUtakmZubi9+SMGvbrh0E3fDGQX0H8NjbH3LOVbfQ6topTNvUWu6mrK5dnD8PPWTvtHXKCuaH7ax2B/akGLRC9lv+jO7Qex1PnQJI5YPFhfOprbZ4W0IhWF9PSf7ccO8ecvLx0ii87zuegCb6AZzp0zrv48UbeHbuutgHz3kbjAh+k2kSCmm069ApzHOo+nyMuvh6eiZnOMk2tlGkKJFe5NhrXNc2zWjWiMbjs3PXUfbHDnTDZNMf0d6tFWVFhKzSnLYx7Labu/DVawaqkPqsCT2zG/xMPAt36YJ5JPgUkrMHo/pdQ8bn95OcPSQsecSmmdgOoN4pmRx56liPoalTmj8v6pvbt1UiA5Jd/nW877ZebYY/EIAY8mx/dslB1w0na1/TDY832Rt5MFle1nh59WHDx+G3yo37fD7H29u+eYB7b7qal6d+zv3338/TTz/N/Lk/saikgCUlhbzz4tP8vLgkSs/Wryq0TvSTk5PDlVde2WAWvQDSurRh8lnpvDEumdP67yuNDgV6JiVHn7Dtdz6f9HKjz9Rk/L4Yvn+B3ut/4raXJ9M3JbqYSocWAYs65vZt+/dmfoU+qZk8/c4MLrz2Vp57bwbJVjJegqVoAa53KRIffvhhk29V04K89sidMd8zkZ7hkGGyM6hbXP4IPqlnI9UYvUI3TCcasmLxIl764GMmXH8bT78zg76pmU7SXnnBPHSPJ1dVVZKzBxOwnl0I2Lm90qFhKIrC9u2VMRf/nJwcWrdt32g7hEIaiwrzWFQYi9cuL6zrunTUeLyp4eW2cXTXDdNkwrSFMa4lsWK7yeQXHuPeCWNRBJx+8dUIRfDpm7J0tv2cR7XcRu9QE9eZ+e/jE4KU7MEIBGX586Setg2Px+TdZx/lxnNGUl5UwA+rt7Bma40c+45DxF0v6jTDpVs00YPsLSgS0oKULphntZPrhKjf+fcU5TDBiU7Y0E2r/pxnjXQ2CA0YCXtiP9j4taLGKeZkf+6WnRqTy6IdONtqND5cFO6Ia+ZT6NgiIJ10tRr/+2pF2Pu6J+E2ckwo/0IL+R+ReXvqqad47bXXEEIwYMAA3nzzTRITY4Rr/kHMmjULveJP2LbeqaZmKirHjT6TU0afwcBBchEUwPaKCtDc8Mb2im30ScmiS9803nyriK0bq50dlQDJRQ74CQZlaL1Vm3bO4Pxjex37tEqgbbNd1wc0MQnqJoow6bxfV/irnci2cavrFM+bBT3HyBeEAqZJ6kB3V2yHxGwD3jRl6MiWL/Mm8RkmVNZp1IV0NlcHHe9W1Mdbs4u9KG6qbiCxL4bhJQ0kP4cPH02PvgOY+MCtjoarY/ziMRKlQJzzmiIEPkUhwafStln4UNq3dSLbflvTSAPCG/NXk31AJp27HAARSeFLi/Pon5Hl8ZK4Qvb2hmrLziA7rcQGwzBlolkMHdvIdvAHZFlhIWRS6QOvf8isz6YSMuDoU8fQLzWL+pDu8ZribBaw2ubw4aP58TNZNczn85ExKFqJoGvbZmxd41b1e3L22ri3dt7N9/HalE+J/KZ+2iw4fGM1fTu3xGdtSutCBok+qwSy9Q3VaYZMuAzEKGcd2Qz2z4hVRSC93tq+rZ2ytXZCkh4K8eZzj/PDD00vYRsJIVw+bzOfz0naUxD4Yu20LFrQXkEoCLNeByB5cC69reTayE8VQtAiwecUymiZ4KNdMz/rKmrkmFEE6dkD6Z+e5TgJTFMaxYownM1UrAW7U6f4aiWRUIRCpICFjR3btlIf6oYSUMEwqa4PyZK32J40OVZsibjG1l5FURwD8vNp73PSqHGcd/m1BHWDihqNZn4Vvypo07Y9XjPs1HMupl9aluNhFwjSBx1KIBBA0zT8lppHm8To+VwIOCOzGy97pNtiwe8P0C9jEFvrgBURbwohq3T6/bRu2553Xnqafpk5pGRmW3OEcOYPe75sUnTO0AmFYGHBfHQT7r5oDKGgxoxXn+aIU8dySN9kXnvkDrQDM+Dkm8PO9QUCnHTmRXzsee2gfqkMO+5kktKy0HSD9EFD8Pv9jqKJsLScwXSkHIvz5/EGksv+wVnpYZth+xlqrTlKCPl9L1y/g27tmtGxZWyqFrhc+6BVXCs5W6qhhJwsT1PSnZS27kkLpsLAMY033K5g7rtgSj32RQXzQZHFj3TD9dwCzvhqyHssmvi9NoZN1fUI7DLRtqe36TBxqYe1WnSUwkuzjDSIm6pc9Xfib/cg//HHHzz77LMUFRWxePFidF1n8uTJf/dtNIrc3FxZsMADE2l4Jmdkh3nCMnKGoBiucdYvK0fySz28HTlZyU7XLz2Ly26/30nqeemh/7HY4uxW1Wus21azy3Ini0sLmfTi0ywtKYyWRfrLYHsorM9zrCnFmrCsoyxvRkg3Hc6xYZqUr98RHio33FDajjqNzdVBmcUehy/nekjkeQ1GKyONDCE4dtTZ3P3aVJLSsmRCinXDoVCI6a886SROKEJIPVPbSPQ8ql+F3p1aRlULAyj7qQE1CQtbNvzJsrIiOu0bXWp4859/sLy8iPqQ7njgbdhtuHZbjfQ4CJms1xj3XFEVThp3Dk+9M4PkzGyHMpGUlsnldz1K7smjWFKUx7KywrBnlfxT+XeCT24g+qRmcu+rUzn7ypu57clXSc6M9l7n5eVx/TkjGm0HgKqKirjGoK5LPdSQLvtPraZLL6LpcpLrQjp9Mxo3XAt/+AojpDsRHG9CnRDQ3O+L8DJphBxOatNK2DYIz8bL9dDKgh1RaKzgya7g/eth0xp8gQBJmTmO56oh76pA6ls386vO/Sb63N+dTaT1u6rI4+P1w82b49McInHUGRfjD8Q2dCq2bsbxEuNuGPH87lCUzMZzMrp2d1WIdD3E4qL5jsHbPKA6HuIdldsc77BQFFq2aiN/95gRyRlSLeKMy2/imXc/JjMri56dojmtQgjOyz6AlP1iVdhzUX/ZFHr0S+fJFbGeQaAIhZPPuIDn7r+N1558iJvOHcnikkKP0oM3qQ1HaaAxqKpK5uDDWFQ439E/D2lBvp3+Lq88eCuaFsQ0PZvxWsn7P6RfGudcezsfnOVShtYtW8R7zz/KsrIi/KpCSmY2kz/9mksuuYRzL7iIpHS78qVworT9M92xPOP151gURtmTa6k3omTPib9U1LJmy07+iJX4jJtEfP1td/LcezNISpUa/vacAtCydZvwk7R69joKJG8c02Rn1XbSush+8M2KzazYvDPMGHYq8cawgnfWhxyj+teKmqj3dwVhnH7bqbcLFvK8dRU8PmuNU1Y6EiHrO/p48QanRoKN4N9mtzQd/wjFIhQKUVtbSygUoqamhv33jzYO/mnk5OTwxvTPycw9DkVVEYqC39ptRnaYlMxsjjlltPN3zwEZ1ARDXDSt3HnN0eu0Ot32igqLyG4Q0jRLyUIaerph8vPWpod45s2bz43njOStpx/m/kvG8v2H7zLr4w/2tAkahXeh8AcCOHtNIXkItpalLdfjyjDZRrG7S63VdIKWKkXkYrfozx1Ru8tfttWEL4g0sghGGBqKEBxxymj6pkhurS13JhQF0zBYtGAOD14yjlULiwiormqF8JwvLA9yLOTl5TH3y6aEk02mvfwkW2OUuZ75yRTuvnAMZUUFzgYjkm5hbzhA/t7YZPbwK+9ww/2PS8kuwrPJl5UVc8/FY3nn2Ue49XyZqCcs8rXAVWBIsLJ4FCFVC8ZMuIr+qekxvYazZs0iVNe0SVsabbENwl/XrHSvbcq+pFtJIPbrugl3L2p8Ns864nh8AX/MhDohBEn7tnK8TDYP1edwUptWwjYevHQdr1auIuKEGE2D7r377fbneXHgQT05atTZ3DVxGj2TJbVCUQR6HAPZ9narwq625/YDaRjb9+5GVVRFEGiAWjRy5Mgm3+83nY7lpmcnxXyvdYdOHkNJjv+SogV88MozLCkpdOaXoLcmbgP47Zd1zu8+n49TjzuaA9o2QwjpGbe/GekdTpBGXCAgKQPC3SCAbJd+6VmMvugqkjOyGgyL2xuQxlDdgPyVicmaZYvDEifL8ueFhei94flQU1yNQnDEKWNJz8ombaDUt3YSiU0TwzBkpM1+beuv8Kbkli8vXcC309/FryoMCP0Khm4Z1yEWFc6nmV9FEYLBgwfz0ksv8dJLL9GqbVt5HUVhzKU38uhbH3JQf9fAnv7S49xy/igWWnOhTbewI7ORHtaK2iB/7oiftDZw4CAuv/YG+qVlOefU6y4tx2sgpyVUcsQBu8C33g38vHwJdxzVk3ZW5Piaj5c4fVsg3MQ5z/xvY9mmKmcd2FS9Z4a822dc+lq0fkrD+H7VFoxYEhZAl9aJmCZR1AyIT7/7J/G3Uyy6dOnCDTfcwIEHHkizZs045phjOOaYY6KOmzhxIhMnTgRgw4YNrF8fzYH5q9Gjew+uuOthViway5pFpbRIDFA0+zuU+iraDRtMdYJPeqF21NGhXTtYXwFATeVm/qwKUVXvTmq1lVvYrvlRFRkW7tm7Lz6/n5AmJ+SevftSuWWTsxiZJqxXmybbMuOTT9CCQQzDIKTBzOnvoLfq1fiJe4ihJ5zGzsoK2nfsyNEnnsL0FdtZYAAt2iFOuIFefZKo3LIJMKmsqndCtpVByVFShYJmGBgWjzBkGGh+H0GfoM6noghZ110R8KeyM0zpYfXGKoK6QUBVqA6GCOk0bIjF8MSV/PQdXQ84EF1VOfDAbtz2xERmvP0yCwvzMU1r4zJ3Jhn9eqPpuiw/WhciJASqKggpCkbNdjZs+NMpKGDj008/Ra9pmpbmwpJCFrf5HvodHf7GVdPRnh9L2Zzv6XpAN/yqQCT48KuKlIdDllq1jRWzNoihBWlo33tA1wOo3rYZRUC9qhDUDeo0g5BhUvzTd4SsfqQFoWTOd+x/wIHUaQZKQCHQzE9QN1AR1NWH0FWZkKEKgb5zOzu2+vA3T2C94T63qqpNjv0dcGA3Ou/XJSYz6OclpdQmHYjPMs4UIai3ksLs4hs1WqjRKbY1tQw58lh6HNiV35aWctJRw+jWrVvU/NKtWzcmT55MXl4eOTk57KgPMWfufIYdOjjm8Taa4iGt2rqdupqgbDtFoKuCYMiM6ckVisIRJ43gjb2wdvz26zrOOedsDuzWjbrKLfhUQaDOh/ArrF8fvbDu2FJF9/bN8dUqVNYJOguTxduqMAwT3a8QMkx8iqB7++asszas9SG5STNVBVG3g/Xrw6lzJ510EpdcchlNZVXv3+UAWBLNc11UKVjxy3p6dkhEEbBqSTkPXzeBkKbxwUsBHnjhDXr3T6Guup4VOzRezd8Y4+ouvFGwkaNGc/BBPdixdRM7ttYQMgxnvHTr3p2Lr7uFOTO/IWvYURx0UHd2btuE4fc5EQDdAM0wUAyTHfUqZk0l69fHNrB2bK0iuH0zEF+eEqB2exw5OCFQfT7SBw9lackCQiHw+fwc0rcvO7ZspM6nohkGIcOw3oOauiZozZomB3SXbdC9e3due3IiP375CfO++cwpB3/mFTfy5446vgY6/TqfzXXuuH/32YfovM++nNKzFctvGIeuyAJNPfv0pXrbJg7cpxV6VQ3rqyz+t+ZGTI8dMY5En8KOba56i2HohIIw/4dv6HLggQRDBkHdpDaoE1IlPzzkVwioKqriekLjraGrNlcTMuQGyo4y7AxKz/p3hYso3+AayNcem8qnm6MTkPcmsg4dhla1lXYJAlsKe/uWTazWdrBT09leqxHUZT9cVLWVlgEfB7STkmiVm2W7a9Z4bKrdEAuVW3bgUwR1IemVx4Sqml0XDqir3Ex9jPOOfXUBNw2KTbOqq65m08YNBHf8e8pP/+0GckVFBZ988glr166lbdu2jB49mnfffZezzgqv3DVhwgQmTJgAQGZm5j/iZd6uVNFcryLQsh2VlRXM+HgKuq7z4dsTSfrqG47OPQyAjvvoBBZVAdJATmjTiV7tANxFNKFtB1q0TCDgU9ENk/Rhx/DIWx9SMG8O/TJzSBs4iJYJKqpQ8KsC3TTZf/92TbrPo48/kWeeeRpT0xBCsG7NSkjr67wvls+ia/owfqvZuyT4vO++QAsGUVSFAZmDuP+iM7ns/QWsqgKj16GkDh1Ei4CKacJWUeN4Hlu2SgBMfIpCXUjHMEyCtSGEbtAi0U+iX7G8DHKxEQI679sGvycG/XuogmDIoJlfhTqNOk1HTawB4sh7RRjICQkJZAw7mhbtO5PgU/BpOlmHH0Prdu255fxRFq/WT9rQo2jXqTP1VklrvUZDtbxKCT4FPeBjv/32C7s3gOHDh/PUs8/T+H5ewMh7MPbrHfNdX4vWpBx6FP42HfhlaSkrivPJOXQovZLTHe9JwCe50eXz5lFVuQ1adYz7aVW+VnTtICsB5nRvx/x1FQRCOsGQQeawo/no7VcIWdzJjKHH0KJdJ5SQTsuAjzbNA2i65JiGaoLOM6tCYARUsvseTMuE8ClF1/Uwuk1D0Jq1pWPOyWyIMR9375dGYtsOqIri6FCrikABfJahvqyogIYYcz1amDx02hBChkn60KM5dfjJDNi/Tdzjhw8fHlY2+qzRTaOKNDZXra1PoHZHPSZSGcGvCvy6aXlyoxUbglqIyzut5YU/O0JiqybdQyyYhsmn777BmEuuo3v/dHyKQvtWAXyKwv77RyeL/alXcsB+rUjwuQVUftO2oZsmzf0qmm6SfWBbFEVQoci5r1YzLLULgdjpj9kWXbp2gW1Nu+cFDRx3//xNTDk7A0XA6uXLCGkh6SQIaaxctpSUQ48iQa3j4a/L43KZXbje0SFDhrD//vtTVRdiq6hy+nyiT6G0cAGvPPkwWjDI0vISUjIH0S8ti0S/SocWAbbXarLErqVbHFAV9Co1bp/4tvBbfl1SCgcOjPm+jUDrdkB0lKltx324+qUpDMjIpmdqFqtLF9A3M4fUjGwUAc0DPksiTv5L8CmEajWgYd4zCDQtRNuO+5AY0kk77Gh6DTqcY8eew7KiPFIHDqFbv3RCusG5wHfNlvL6j+87Z1dVVnD/1Rdwz2vTufXFyawsXUCfpCSyhh1Dol+hS5d2zvwxd958Nqz/A5DrXULbDiT6VEzNwB4Pos9QfOsKyDniWFq170ytZuA3DPS6EAGfLHnewi+pMAFVcTabrdq3olVitJnzR6gCRQhqNR0lKKv16fUhVpQV8e6G8DmhdfuOpB56FNPj5zXuFk4+5zJ+WbGEocedxMgzz2VnMETLZluhUm4WWnboRCigkohAb6ZTFzJQFWid4CPgU9jfmrt+0yoQQmoVy7HcNLshFtbWJ5Lok04nLDrEzkCQxvtLOPod3I28dRXESoSqNGNrHfubt6DzPvs2yB//u/G3Uyy+//57evToQadOnfD7/YwYMYL58+c3fuI/AEWRovgPXTqO76a/62hjhrQghfPnOMfJ5BT3vBmvP8eaZYvCrmUYdqlT0wl39UnJ5NQLrqRnSqaHI2axxhqY0CNDdpou05RkWNHAOP56ONSjh1r6Ob2XTd/NVogPLRjENA30UIin7rqR5+6+CZ+nHZw67IRn4rqcOLlDXVJayEevPcuK8mKrRLXpnGfLNy1cv4NNHjkZN6Qmf9F0s+HO7OG2JqWk89U339I3NctDmxa08KskeaqB/e/lKbK0Lja9QhpktrRbZBKfFzk5OZx13oWNN6IQEMc4Brjm0Yn0Sslk1cJi7r5wDO88+whXn3Wq5KwjaRVYPLGnS6saNI4BVEXxhITlc7Twy4SsfqlZPDXpIy669laenPQR/dLsZ3cZlorHgxsWVo5jl+bm5ja5tO3Tc35mcSh2Rblg2/3RQga2lJNX9zaomywuLeS+i8fGvfbg7u246+RU515VIaj/hzhvwpIKdDnIgraJPmIxExRg85+/s355qQxj79EHCxbm/8Q9F491KkQG1PC5y4tDOrYIM46tS3iUTVxaSHO/zx0XwMEdWtA5zkI3dOgwlE+iC0nEwqQY8lJe2BSbpIwcfH63FHnqwMGubnBTeNxCcR5q2zbXKveOE4CSCI3k8oL5IGSUq1u7ZmFcbftnvF6Wl5fHxWNOpnL92kZvLxTH6VuptGBbvclHrz2LCVKSMSXTUa0Ab2Kb5OsuL29c6UX1+UizkqwVm2KDoE9KJqMvuop+ae7cqSqCo0aeRdeDeoZdQw9pzP58OsuK82jdrh3Ly4pk5UWrB+Xl5XHppZdy1JFH8OvPrjSjYcCs/CLOeM+VNzRPuIG7X51K//QsV4LMWi9NIzxx2buGrtxcTV2MEuP2d2LTCGzJwKXF0UohqpD5FnsbZ159G7e9+B4njR1vzUkKiZ6S2yHDI+1pU2TM6P7kNQfi6SQ3Fd7S5XYJ7t2p8ju5bD3XfbY05nsTY8g2/lvxt3uQDzzwQPLz86mpqaFZs2bMnDmTzMy93/n2BsqLCpj2ypOWISg7iRCCQCDA4Ycf7hwnOXnuKjP1xceY/t5bcN5E57XfK2toEWjlGIaGabphDHCyVg1TXqehPlny+3YOaNuMzq3kAjR71mwpPWQPokPCE5VURZFav932qDmioCgKumUEG7rOZ5MnIU7cB3odaj2DzQ91DV7M8ApPi0oKuOGckWhBDZ/fzwNvTCcty032so1gw5RJWJpu8HtlnTNB2oZSSDcaSbc1rXtWufrOBxk8eDDPTv2KhQXzSBt4KD2T0x1OaN/ULA4ZkCHDxdgLnUDBlB4YwyTgU/ApAqOBz1yxZCGkH9FgG6rt9m3Qu9Wj7wA5cRd5CnNoULZgPr1TshydV58i2Kw0nOwDYNs7Dg8Wl1/qU6F/ejYplgZuVX0oLJlMCGmw2dQAe9NgG3nxEKsISiws2RC/etXUsj/ZUh3k/OwDpS1jmpgGmJahPreglODlU+Oe39yvhhl7AZ+IrtD2N8HZWHj4yHYiqBcDN82kRKvhuw/fR/WpcNq9e/bBuiaNpGCQZcX5lvpC/O8mUsPYvk+7z3tP7dO5Jet31PFLRa2jfFEbx/IelJPDMQO68/WWX6Djnk1K1cEQrRL8HJKcwW0vT2ZlST5pgw6lX1oWW2s1aoI6mtmE/mc9jN8fYOjQYc5LTv+3+ndGjqyGqhGUUZZBhzqbRYBmfpVaTbc2QQ2rZ8yaNQtN06C28aptV368OO57z61SUV54DN9EP/e8NpXeKZn4FIFiCsdgtBNZy4sKuPOFSXDoOQ1+3hGnjCM9W3q1FSFI9KtU1+vheQvWsZJHb3LimRfxyn03OddQVZVZn04hpIUwTQMhFGZMmsiz732M/kdLjj3maOrq6qIMsB8+epe3P/sBjr4y7PVeyZnO89gc5E1VQbq0SQTnGU3MsOd2DUrTlNJjdtERqeghz1m9pZprPlkaUwFn2lsT6ZOazutjUrhoainGXvQr2pEJgYwkeal69sYGQfh6F7FeGKaJcJ55z+Cut5ZsotG4RGIsPDOn8U1fJP6FIhZ/vwd54MCBjBo1ivT0dAYMGIBhGA6V4t+EvLw8zhl5MuV5P1mDW+DzBzjl9HNiFgLwzoNSIid813rLlyuc0osGbp3yzTuDjkcMkzCDGaDSGtBeSOPavX5K9mB8fn9cQ+TC2x+mXYemyys1BQ/k7se5t9yP6vM5n2uaJka9m1xoV6dcVFzIJ28+xzfT3uWj15+TBUSsCax0wXwnuSQU0lhUMM+TiOAmCxim1F1cs2UnW2vqIxJQpKbwjBjE/0icPO5sktOzWZCfz+3nj+LdZx/llvNGssLW9yS8Up6zCFh/Nw+ojufN9pjFG9fHDz+10fvRG1m87a++b6SHzJIm0p3IRNOgCgVvb7WNNdtItp89wSepDDjeYrclhBAk+hWPcSzbJtambtasWbvlgYiFH1Zv5dm5a5lU9JuTkGOrWvh6pMY9r60S5JT++4Z5PiM9o38nvEltbhESaSxPGHSgc1zP9s3RQzLBSdd1mu1Jda8vH4ctMkyqKApJGTlh7bFr92+d531NEc6mqTHFiKKCfL77fAbU7Xk53wunLuT1Bb9iGCaHDMhg+HlX0HNAOppVyeviDxc1fpFfFyKKP2bwkcfz4gefMCjHlfCMjJSkZGbz3XffcdG1t/Lsux9LDWigV6eW+FWF7u2bcVCH5laUBVol+OnQPDanMjc3V46r+j1vB2f+LMxzVH+8ETZ7CP5QtBCjEeMYYNjJo1A936Mq3AIh9hwAdqKpbKdjR5/FhDseoWf/VHKOPJ6jR56JHtJxiqaYhqy8mD+Pn2bPJuhxPHk7YcHMr8LK09v46LVnWVQi1Z6CIYOZq7ZwzSdLWLyhyjEO3WJK4cYxQE1Q5+etO50yyuA6q0r/iF95850Xn+Dui8ZQ+OUUjMqmVbVsKgQ4VBOfImjmmZe8xrBdH8A19t1rmJ6fe5ro5qy72FHf2MoZ/7/gH1GxuOeee1i+fDmLFy/mnXfeISHh38M5sTFr1izqg/U43k/T5IDuB3H8iHExNVC9HVPI2GP0MXaHN0E3DL6dV8SVMxbz3o8l2ALuXo8pwOot1WyNkHyL3GkdkpLJna9MIWvcFajN20R9brdeSeSePGqXnr8hHNiuGQe09nPAIX04duQZHHb08fitzG4RcmkQhmmyqKSQa84+jWk1PXij+mCmvPAo148f4cgQpWQPRvWp2BWF+mcNDqea4G4adMOkqj7kvO/VBJ6ztnFSo8/v5/gR4xACfpo9Kyzre1HhfEtJAMcwBJdS4HqTwikGPkUJW0i86JvUf0+aGYCNVfUyqTM5nTtemcK4y2/i4Tc/pG9aljN5BUONec8lHj+przMZe6kRdqKbo6pgP7vnGNvLYRvQPqdd7CUzNhwjYC+h4NdKvlq+2SkSIikmJvt17R73nB7Fb9CpRcAx9AXSU7P37mrXER6ZsCk7ghP77uMc42/R2tkUKYrqFuLZHSz/yfn1lLMvpndKhtMJdkWfXxGuNzWWIez0qwauMXv2bFlWfC8YhgDfrtzM8s3yWrYDYmd94wmbNkbuU819L7zB3S+8TYpHqrBFgo+++7QM8xDv1zqRww4dwvjLrqV/upQHE0LQ2tI5tjc6dsW5/VsnOu9FIicnh5NOPrlpFJAmQAhBv4xBVmhePr1tJNpz5YG9BzR8EQu9UzPD5gHb4LfpJl5VH3ujpwBHjzyLxz74kruef4sjTxntKANZN4iqqmQMGsKwXKkSE2tuqMu9JKaiy+QXHpP0spICdMNk+SY5Hn7ZVkPIpll4jDubyueF7YW13sVAqlc01F9NPYRWX8/Mj95v4Khdw5CuLZy51lYHEkIQ8HAUDc/3aN+34XlOgOLfKl2nmnXM1p2ejccuYGNVfdjGwm7Hv0t+7d9oiP9XSS8OcnNzUSOM3LWrlnP56SeTlxfNU/IOsJQzrkHxRRv9hmE4HfD5r4uZuEae9fmseXw97d0wzphrmIdfo/i3yqjr1mo6rQ4aQMF+R6Nf8k7U+y/870p+WxOpML/7ME2T1UvKefCScXw19V3m/fAth58wnN79U8BTcntZeRGl+XPRghq06wK+gONFKFswN3yy6nAguiH4eeUyl0vm7GC9ZaU9k5z1+pKSGCVYY8AwDIcukJubiz8Q8HhkhwCCFWXFTJ74DCvLXY6mvdi5C6UrzdWzU4u4FYDy5/0U8/VdwX3fr2LmapnB3jM5gxHnX0GPfmnOQmApv/FuceMVrQ7p1MJZ2BTP4gfS4HUXPddwEpa3yDasHY+adYwqpEyV5FrGnuD2lgfZizk/b5PjyfYSNbDC9RmY63jH7UP/yaJNXn6q4tmcqEKw0sMPfefxuzj/5ns5+8qbOeq0cexJADVMu7d1a+c+WiWo7N86dtJM3HsXMsKQtG94wqDtcW2sbTt0sHjmdXuvWtm9361ypDHlxinaOIqHbRv+8HhEw/tx84BkIQog0a/SpY1sK0UIS3v+KZaURCsceL3ODeGGG250Dcg9hGEYzPpsOqsWFnm8j5J2V2fx9w88pE+j11Gm3S4lHnHbxObNK8KtkmgXiVHcgxDIJDJbAvL8m+6lR99kFNVlcwrhahFffPHF+P1+vAN4tdaSkRddE+P5pDOjdMH8sJHgRGRNl9Zne5O9fUB+ty5P2TQlhWBHnUbByuhKpp4PxjRNfl6+mKgxqDeg8FD8ScyXkxN2sN/Sj/n+w3eZ8fpzLCktcPpLwJPs7dUSNqzv0s7rseEYtKZLjVi7bSd1u0Ef+62yJswdbTuiGpIY3Jv4F9rH/xnI8ZCTk8MDjz0dNcuFNC1moYCN690M47J9Dreqb4XD5vKYwLzfPZJkJrx0/y0stcJH4AmtRHQa22vovG+Y1Gk61XXRISkbv69Zxcv33hT3/V1F9c5alpcVhyXpffvxNJaVl2B6DOTFBfNJsaoG2rCrPqUOHIJpmpQXzEMLtILxz2HmXsirD94mKRi4k5jXo24bzp988DY3njeaFx+5h1vOb6J33DQpXTAPRcDgwYN5/r0ZnHPVLTz61of0z8hiSWkh140fwaRnHuF/F45m5cIiWgTc79H2oiQ62VQNr4CDhgxt2n01gtcW/MrC9TucSd+m6phWY9SHDF7Oa1rig5ceAdDMbxsA4Z5xcD2Eiud1e7G0jSS/qoTpxEZib1IsvHg57xcnalBZG2Lh+vgh0tzho+V9e7zG9obnn4AlLe20o6rYBpjCD59Oc44LaUHWLlvEuAlXc+QpY2hSmCAOfD6fo907IFsmXwmkAbh/m6ZXMe3YIsHpD838sWkqjTXrV199JX/ZSx5kG28X/W5RCuR8MXvNliad9+OH73L7Oadw9+Xn8OeK8jAjBXDGhVeveHFJAVedeRqvPPEgV511WpTTxBOEahCKbbXtKdJPwTQMvvvwXR669HSWlRa6RiJSfs80oVE9mdV5mL8vZmlRnjTYfAoHd2jheo2FpZojsOYRHK61PZ5URUFRYEV5EW88eic/LynH0ENgysI8JfmytHNOTg4vvfQSJ554YlSniaUL76WXrdqyk6+WS0lFOw/DjsLa8Ors27CdLq4RbXLbJ+Us3dGgD9k61wynOa3Oh2dGwOpYJcBBzH075uuLy0v48PXnefX+m/nghUe57PTTWFJShCIIU0OyE/PsxORazZDJhJjO/O+NpnqpibsDx3NsuuuLiYx2/x1oih74343/DOQG0Ld/P/qmh1cH8/n95Obmhr2Wl5fHF9PeDXstFKNT6ZZlYxhm+EQlBIZuWDtjM4xLFX5+9HtB3eCbFZu5/esGPMT2jr+ycS+jF2ntYg+MYLCevqmZsdUJgq4GY1JGDv3Ssrj/NXfRH33pDdz36jSSLIH2/lmDURKtSadLEoZhUF4wzzk+MtHPME1mvP8WT/zvegrnzuK9V55Fq2+aOLpihfdsMyk5I5szL7laJisBpd4M9fp6fvp8ephnyfYeN/OrTVoAM7Iblm7aFTw662c+XLje4xmRZcXtsFtTsKKs2JJ+Up3y3f32bUWLgOqEhb3rlKrY2evhHmf72ZsHVEtuLdr7YSM3N5fAX0Sh2lEvvTeP/LiK+b9UxD1u3dJyi8og4Rj+/9Ds50Yk3HLl9k8AqrZAsMY+GCFkpcPE5rtfrMAwDI4acQYPvD6dpLRMh0+6q4tp9/bNnbBwzGcT0LOjLAkeD46G9F5eeN2okyxF/sL8Xxo+YcY98MLp2JbF/JlfMfqk48jPzw87TCAVKg7u0MJ5rWTBPEt7XqoaRTpNBK4UZEOYPXuWw9HdI+ReAJkjAEEopFFeKJWhvLS9DVX1vDqnsUiidGD0y8xxNgbNA6oT4bCLI6nCKo1ubTSdOcLaSLdJ9LGocD5afRDvDkBVfWTkuCXp8/Ly+Pzzz6Pu4ocPoyOhoy+9kcfe/pA+qVnc9uVy53XDMNENw6O6gPxMqz/8sq2GCkt33Bu1tNtnQ03Dg8Bv0Zx8fr9VEMtCIzvB0Zdcz3GBaMeFYXiUmgyDkBakdMFc3LicfW9eukh4URRNNyj5fXuYQew1mncH4bxsdzzpf4N9fHPuwRzWI1pq8p/GfwZyHOTl5TFm+AksLylECIV9uhzAoGFH8vKUz6I4yLNmzUKPCGmIGB7kZeUlmEjjuVnzFp6DBT6/nwFZ8rq219T+fXN1kBWbqin7wx4Q7ggI6gbfr2rEU2IN5NC713NQi6aPnoXPXx/zdcMwWFZWxKBjhke/6TGQpXydyUHJGc5rGSMupHdKhrPb75OSyaiLr7HuU1Yr7JeZg80fM/AMWMuTPOvrz2I+X2PolTSA5MxsUru0sT+ORL8q6QWKIGPQEFnYAtnGP34yhSWlMnwaxrnzeP8awt52nE5fuMHxGhum1M09oG1i0ypjgSyfiwzrR8LxDguvMSwIqKrD2wVoneh3uMd2W4Cg7z6twiSKbOTk5DD90692n8qdcwAAmPRJREFU74Ebgb0x+H1bw6H6lWUFnsRL+XwtAir7t26653RvIq1LG4dWYWeu233pqFPHoL59Gbx4Jj5/gCOHj5HeS0UhufPuG8h6SG4m+qVlun1YwJadu155K3m/NhzcIfpebH5qLPULLy644ALrt707QGxPm9xENrKq12yHtcVQH953gsFoY1cRsjqgd1OQMWgIfqvSos8fWY1RnpPWtU2DVQUBOnZsWJZxlzD0XES/I/D5/PTNyEG3KEghq0Lc0z/93KgWfofO+3Df69PpnZrlJnFao9+nSilM2X/cfAWbPws2/Uqek5o9BMW7URKCE0adzrGHD3XmmFmzZqHrOlT+GXYf5sm3Rt3bKedfLpUsIl5/v3Q9n80tDqMaOJ5iTKqDIX7eWsPyTdWW99hb1dZsdKN89rV3ctaVN/PA69PJDq50qUGbfm7wvKTMHH546oboNyIWDkX1WWoohHldvDzyWisR2/Yog2sjOB7xsA3CrkHTjYi2MdFNg2DIaDA6t7dwRM+OTZYD/Tvx77ujfwlmzZpFsL4e0zQwTYMtG/9k9PgLwpI4bOTm5joymjZ6JSVHHffYdRdQXlRAyDDDEhPbdtqH216eQq+UzDAOEMjOX6OFqK4PufQK92003aTRyIQ9YkL1GNvDs3Cb+eN3AV2PzT2qralh+uvPk//tp9FvGi4na0VZESHTpMKTZHjzF8vZXhdyEwBCOglJwwBo2a49t708md6WjqcTNsKdBMBk6LEnh33kiWdfHPcZACj+GIDhY89CEO79cgpOCEFyZjbDx5zpeI11XafMCgeG8ZAbSFL6q2F/9zvqZLKiX1XQm+iBGpA5OCwJz4Y3E932EMvXZdgrwac4C1+vTi2JmMcb9aRnDhzUpPvbVbxfup7nvy2hriGtPSApbaDUr7ajAYqgbTM/Xds2nXu7N6Eo0gvnbkhc/ne/tCzufXUK4y67kdtenkJSWiYBnyCgCk7s0xnl0wd3+3NtwwWwPIG7138DPiWmh7iZh7rTECZMmMA1193Q5I1tU7F2Wy1fL9+MacauShiGOJ+tKEpUhDBp31ZRiXapmQN5/r0ZTLj+Nl54f0aY06RVgo+enVrQGPLy8rj88ssbPW5XoB59Gec++QG9kqVMZcgwnWhmUzyLLdu0pW+qTD50S6JbnmS/is/mHVt912cZxvbfqmf+6J+exSW3PySVjhSFhEACJ4wcR5c2zZyxmJubKznIs98Ic67EgmFIqkisiNmUn3W+mvqO85z2POn1GDvSZZ6IpKabUblGkdi5o4KxE64iKS2Tg9r44MXT4Z1rIH+KPCBOuy4vziekxac+ghyHw0efwYCM7Kix4/UO25Kw4c8THiFwHee7ZiFX1mosXL8jTHO5TtMJ6iZTy9fzXmk03WVvQxWN5y78E/jbdZD/X0FurixwYFiTi2kYLCwuYNjRx0cdm5OTQ7eDv2Sd57Uu3XoQGcwKmQbF+XPZv08qWrAO27TYvm0b0N3t7GZ0UppNVfMmrAFW+ckGetbsN2CjFGFXfT5Zaa2jzJRvppoM/PkjZtfvi3lIDCNmxyb46G4YcXf460LIhDdFal2GGdIeY21J8XzSs7O5cGp4CaIaTXcmsDcKf+PzpdJorxbNqKg3wnhQhmm1kifcdPK48YQMg7nffs7go06kfbeefN6QmtPsNxh64khOPePcsLZyPCS2Rxg4ceQ4Pp8+GU2TOqfpg4aQ6FfpZyUkLWgglB+J4sIFTT62qVizZSc9O7W0JkPZD+IVEYhEz5QMVEXK5UVCINu3ZcBH84DK1p2a4yW3uaaRdBPbe9zYRuGv4CDbWLC18T1+UmqGw5MUEGYs/1PwKWAY4QaIELKdeqdm0TM5k/qQzsryYlaU5JOcPYQ5X07H2Nn0/uegrhqfP8DhFhcb06pEKJS9uslrneh3qDuNoW3btsQsp5c/BQbFL/jSGN4t+YMje3Zk+sI/GzwusHUtwYjXFFXlxRdfjIoQxuJap3Vpg25mk5yZHbWwe1UtGsKkSZMIhWIneQ3f3+DT9bvuvwoJH5N+UTigRRHLS/IZkJVDSDf5sng5q/x9Gz2/ddt2ztg+oG0z2jbzh0Uc3MRdyWdumaBaCWGyEeyCMQuLC5gx5X1ChskFtzxAbVUlaSnJJEdQFnNycpg1axYXXnolS38ugD7D4t7byvIi0rIGosWk5ghefuBWevZJIiN7YBidwjTl7Tl+V+sF6XzSrbkgzhyVP5naXm2dqFv19gqEUDA3e7zHy36EnuF95vUxyfyxzMTn90f1M6H6UP0BDEPH7/dz4qhxKEJGS73fuC3tZnvChXXP3hwkL/3CtQ+kDF6CqsRNIPciZHmPbdPaRBrkihCs31HX6Pl7A3Yexr8N/xnIcZCTk8Ozzz3PlVdegWEY+AMJJGdkx10AWrcLrwBWWxvdsXz+BPpm5qAZBtWVFdBMcm7Mg7J4c9EW0rPd0ImBK+EiGcuuB9W7s9wZDDW887K8pwcc1Iv1v/7M1g2/Q0c5UdburGb29Dcx00+BSAP5SUmfEL+URk0dyldPgKLiC/i5/q4HWbF4EWtXr6C8MN/ZUIDU7tViSMQY1mg2TNhQFR7ifWalQufmRQwZnIPN1I5MrDBNkxPGjOfkcePZXhfigVcng/+QuE2QlDGQax96js4tEyxReQnbKHZD74LkjGyefW8GhfPnkD7o0JgRA6cdGhnR+XN/AtIbPmgX8b+vV/DYSX3p0jbR8Tg0Gk4G7jmmJzuDIfZtFU0raJ3oo7o+BJj02UduBEyzhg1V9WFeJNsz0b55gM3VQav9zL3tCNyreCjNjRDY96kI4VZZ+4cgKRam0/fSu7Yh/5cKx5tsCli9qJiHLzsdLaih+lQ5tjoetMufNaDkFUa8MoWktCykXrXpiZzs/WdrCoblDkP5cR1RPbdi/R5f+42C3xqUfVQKpxPMmxL22qAjj+fMCVcwYcwJTfoMxZPw2b55oMFjdwdfPnUrjH1kt84N6Tr3XTyWkKYxxdLVDV3RtEqqIrGls4lsHpClm0O64XEiWMaxCaa1OV5eVsTCgvkkZw8mI2sgi0sLueL04WhBaRr6AwEefPNDknr3jDlX5OTkcN1dD3DRq9806Pt86IqzuGfiB/QckBH9ppARv+8+nkpqVrbrUCHcmHSr8AlnLYk7BGoqYf77fDofunfvwQljx9MvMwd/IIAWrHc3/qvzOWDOM/x22NXOqQFVoXdqJufddC+vRDDAUrIHc9oJA1ixfClDhw4jOUOuMXJ+de/GNFyKhel5SzdMUD1UTDOSK2yyestO2jcP0CMGFSoWbOPajtZi/fy7HAnC9bj8q/AfxaIBXHTRRTz7/qecfdUtvPjBDPomp8U9duumjWF/5835MeqYyx96iV7JmXw//V3qdobzen5TOobJ1GBa5S/xJp+YUR7knfV63IQZsEKpgQBdexwseYheY+q7Fxr17kUbx49z4TlnM/rCy3nwjemMOPNcWrRq5RjH3j5+yIB0p+KRF27ygRlTQ3hJ4XzqQy6vyuZWgbcd3MmjuAHjGOTmQCC535GcQDfELei/XyuaB1SS07M5+9JrSc6QlcYSPed4m7qx8Tzo0L2jYhGJylotjGfWNjH+Ap3epTUvjezPIZ1kImQsTuR+rRNJ2rdVlDfRplV4+dcAB3mSlf6l85qDsPu3brZ5QN2t6lB7G4oQzpJo87yFp62XFee7RXQ0zYrU7Pp9j774OnomZ8oseUWqELRN9JPoj02V+DswOGcwyQMPjXo9ZUjDlSebgsY00c3Nv0AofGO+bdMGlEieXCOw+753POwKxo8fH7cGQEjbdW64DcMwnaqbuqY1Gub3wp5XwihUwpW19G4yBbC0tJDbzh/FO88+wq3nj+LzKe/wxtOPyAqBzrNoLCnM8+QsREMRIkwiNBa0Sz+gqLAwrgcZ0+TbGR+wuMRV8MAMN/jsvBav8Rw3iuLpD9989D5CCHqnZnHby5M5asRZjvZ/ICGR0y64KuzUVQtlQnT19oqwTV8fsZnLjkqh94AURlxwJQPSs2iZ4HOoa97h6C0etmZLDet31GFgEjI8nmUPLWJnMOQk6RmmyfY6jS3Vjfcju8iT3U7BkOtJ/js30P9GisV/BnIj6J+ezfmXX0tKxsAGQwAD+kQYaUp0iO2AXv3BNPnodwU6do963xUwd3eNkVQLrxcVYGewYQP5nGtu5aJb7mfBrG/lC3PfgcXfw7OjUH5eIIsQxEgoBCvpJoKflZkzlGNGncWpZ11IRtZAnnvwHt55+dkwKoqN35eVMf3156Ou+/OyRbz/8jMsLS2kentl1PtJGTnUabozgYX98zCsTGB5WbQGaSRyThiJEILK2vCFwkux8GrkeukDPTo05yDPLtxrEDbGt0zPiqFi8dNb8Nae8Q4Nj1cE4idFKUu+4/g2lbRtFrA2IjLZK9Z60Myv0n+/aF1bb/JN2HueNtijeS0UGYDcu1hYMF/Kuwn3Ppv59y61YHcgEDQPuBrbQJhxjBAkWd4qJ4Pe70dsWYeyYs4ufVavlAwnsVJVLFk+IZzqaP8EFAFafXSUbdAxJ8c4em9/ePSyt3JRKdeNj5Zr+yuRk5PDjz/+SI9eMagPVVt3+7qGUFD264miqqg+P+q+DTsQvDCxcg98biqvNI7D5wDbsVDmqYSqBYM8dfdNFMydHZYp5vP7SbeUK+ItVb8tLsRsQmXFNr0yG0xC03Wd0vx5Hok0N/LlTeAD6XF9veDX+JVIfa7jYfWSRawol2tNr5QMJtzxMK9M+ZQzLr+J/708he59+oWd+vBlp7OyvIj+WTn4378WLGrUcT3b0yrRb40/t10TrTnJ+2hezvSd36zguk+XYppQq4VcO8D6V6sZUuXKYyPohsm6ioY3HSC/S6+xHdQNbOm4SAfW0IP+GqWJ9Tvq/pUUi/8M5AZgJy95uZfxcOd5p5EYdCtdKYFoz8B7Jb+zqLSI7Z1ic8GWlxYixTBMp4N7R4ydpe3dFddpOjU7tse9r+SswaxdvgjD5gnX7oBvn0XoGiedfTGnX34TR406O9bDc3C/lKgQy86gzoqFsizz8rIi3p34XNzPvuuisUx56cmo15+/42refuZhbjl/FFv+jJae65mS6QzY+pDuTnCeic6w2mFJYeMLWq+UjJibCNcbAvu3SQyTKbN1P8PLLEO7ZgHn3KbyLcNg6LDtN6je/QVw2cZqgiHD6RpLF5bFPvD7F1ldlucxdKGhCsve8ssOzxg3uStsgfQc1xQjK7sizvf0++JGz90THJJqJ+h5nyF2Wew9QV5eHg899BBFRUWNH2zdj5386O1H3vmmd0omL7w/gwnX3crz73/Kg298yOmXXs+JHXfC5nW7dH9CWEVvhLcymvhL+eENQVUEnffrEvX67iYO7griPbMWR+M+HhJ8KoE4zoWmIicnh8OOOTH6jdrt8NyY3b5uaOyjjLn0Ru5+dRr66U806ZwWBDkv6wBA0MwbNVMEKfu3diMcnn/pg6TOvaz2qFjFewxZzjwlneGnn8MDb3xIRpak8LVtFjvaNWzYMJRQ43zXLgf1YmV5cfQbQjgbyZTswY6agx11Bdc4tp1PdSGDWWsaiDb4XTqaYegsKpjvSWAW9E7JZPRF0nM8/aXwNta0IEuK8+mbmsndEyfTzJJo7JGU7Myptlyoacpk6/SubcKMRLuXeiPG9vpvO8nsdbLGoow5Si4eWsmaLbFVfrbuDFKn6Y7Kib2mhnTDMbQjp/bq+gYKo+wBjIaoLv8g/jOQG0BkWKkhGvnS0kLqyzxyVr7oiaDkjx3c98p7ca8x67Pp1Gq6S7WwSfMeD6rXo6wbsrLOjsr4g/zmc0dQuSW2DNzXH7xBq7btEL9FZ7gFAgm077yvpGV8+pD7nMXzuWfCGFYuLqdswbyoxUZR3AUj6OVpeRDSTSdsHCtkvKKsyNoV647Wr25VSrK5yyay8EpSZnTZ70g4pYUbCO95M6udRJQY3/fBHVvElEmLh+dODfcs7A0+wseLN3LBtHIW/ilpOgtLYhtlPp+ffhmDATe73C4X3Rj2aZVgtYlrAHdq4W76vF7Pvvu0jHkNL/av/1NKa9mo3QGTrmz8RvYCBFLazksZ2dVM74aQl5fHkUceyR133MHYsWOb7IWM9V2oQoRtTpIzshl/2bUMyMgiLWsgIy64kl9WLIF3rop5zUjsvz7foW/YQvy2HNs/6UQXQtBpn33DXrtu6EFkH9CWE/t2jn3SXuAnW58u/68oHNQv1VNRM1yurTH07dySpCb0/cbQtXscXrm2ZwlSSRk59EzJbPrx62c5ORoJPjVshPhUxaFVeNfFARlZPPPuDM69+hYu/99DBAIJqKqKP5DAtXc+yPX3PcGA9GypcKHAIR1j01EGDx7MyDPPafQeTdPktfKKqNeFojDqkhu4a+JUeqdmWUaku36CvYa6xTe0XRD49fkDtG7XgY9ee45V5SUIAWu37WRleRH3XzKW+V96ON5ri1EURWpJI+iTlsVZg3oCUh/abkefGumVD/cg64aU8vQapau31IS7i+xnM1zjP6yQmAkVtbGjdGu37WTJhip+q6wN86x7q1BGOsj+iv30lLPTsWUD/234z0BuFK6Z1NDXV5I/D7PG5RUbcY42Gmly3TQJWprKuhFeDchbRc0wrbKTAgL++BnTIU2jXceOjsC53RFN0yRYX8fE+2/hm+fvxPd8eDW6o8ecS/FP38s/VudJWZudlZjrSglpGktLC0kfdCgJiYkOFWPo0KFhA1xkj0b4ou9NWLI/Pp+fffY/IOr9pcV5mEhJH92a0HRnYnPpJkHdYJ9eKQ22pw1ViTZ47QhB8v6tI15vmFtrGy9NwYD9wq+9t6wS04SHZkp1kqTUGEkrwK0vfUDvVFkUAiuc3irR1yTOZIJPpVOLgBMC7NImkW7tXapJ0r6tpAcUtxxvQ+jVP5mwzdCaBYhtDZR33UtYVpwXFgmSBQ327kQ8a9YsgsEguq432QspYvwG1qJpv2Pft/WfalklOUdbiWSNed/LvmTj9EdZVV6EAEmvwQ5z/7sWI1XAn9+/y5rFJWT4NsU+qH4vlaa2vn9/IMA5N9zDfa9P56Jrb+XZ92ZEKVg0BJ+6dzjcbSIVL754fI+vCbCkOK9pVdC2/ApFH9NSq3T6nKpER1m882L75gEnspSaOZCMQUOo3lHJ1Xc+wK133M2kDz8jxVL4iDX3RkIRgp6HNE4FMYE/1A4x3hEMP+8KeidnEDIMT+lp21A23fXTWkRCTTCQhaIw8Ijjue7uh3j+/tv44PlHeeCSsawsL0JVFBYVzZeOnqrN8NWT8NJZqJ8+wPFnXMTSojxWlhehAAfU/MLourn8sqSMVQuLmPHOa6wok+MyXkTDNKXKRtCTL3H3tysjvOOeNdFqIS+tBOLL+7lGtMez7hxv87PDzzns4L1PsZARzn92wx4P/6lYNADHEPJ4FuMhM+dQxBeeGvFqHKO1gV5w6EkjpWGoG87Asa/nzVgFV8oFU5akjQdV9XFwUjJ+VSFkmHTt2Y83HrnDOcfmDoeCnmt8dA9f/lISPnA3/wyvjJdJfwkJJKVlkZyRzfRPv2Jh4XzH8zL0vBux97vG4DMZ1rUFsyPuyUCgCoXxN9zD8nbtIcIDvrNqh3xe4XqOTWfCM53Q0oYddUyY3pC+m4TADS/Hes9b3rNlgo+dQb3B71oImpwrFbUr/gtmgUOSBkBxSdTrvVIyXW611ZfbJvqbnHXf0I6+mV1gpYnPU1VRAaa3KILANAx8/gCxgnYpCZW0r/6dH/39m3T9eEiyKoL5VLng+ywv6t70hOTm5hIIBAgGpTRgU7yQDm0r7DUcfrDAdCIZYLp0FwHHjD4b3YCZn05jgwq1+8VpIyHQQyFmfzadnimZ+FSBppuW59r8x5NivB+vh0JMefExfH4/Q08ZC11HRp+w1wxk2cYX3HQvvVMyaOZXOWzIkAZzOf5KDOrWljO6w/vrrBdW/LRXrjslMJgp75U2fuCkK1BVH7mvfRg2XxgRg8TerAV8Cgd1aMG2miBCwKLiAq4bPwItqOEPBPj8q284eEAGW2vqHc9xY9OEIqB5nNLlXsSL/JjA83PXcn3uwWghAw051sOjjnaSnrxGUxJ1VZ+PUedfxpryAjdhNiSpfQf1z2BA1hB8fj+hECir5zN0+BhatWrN5+9OxDAMpgUCXHDTvbz+yJ2ENI0Zrz6NELK/f/j2K7z0wSccMdRNVvU2edc2zais06LmKptj7DEHwhL87X+R14vVZva5TvK/Q9+Awt8q+Noq5w3wxtgUmvlVElSVx2atabTtdgVNU1D/+/GfB7kBCCFYXFrIOy8+xcLiggYHede2zaDO5SDHM5BF8zZxr9EzOQPTlFVtqut1y2sanlzgloC0QkRC0L5D/GpMWcOO4tWH7+Czye/wzYfvIwQcfsrYhmesdcUxd7Wqz8eRI87ivlen0XtACkLIIhC33norOTk55OTkcOLYcD7znPpOMRpBytZVba+IeRtfzM5jeVmhp6SygeYtz2m1wbbaxrOzM7vK9g6oSpghDJIDGqnq0LVtM3p3lmHTRo3kRhBrB45Q3TdjYVLTQudexCuzbHtN3ap3jVf/i4TNxY6Fgzq0aBK9on3zAG3btyNsV2HdSLzN3bJnL+fg1nvG7wQ5pgSyoIFPscs6N3l/0yTk5OQwc+ZM7rvvPqZMmdIkL6TrJQ5/TVUiVDfweu7k97iyvIid2ys4//o7yDlsWOwPWLMA5r/vXFiAk6hp02bEXm6HXUXYBkwIy/jQELHGxvKf4MumcWkbxaafQQhL01awsryISS8+xaLigr1z/V3EsrIipt/UdL6xiuGWIt9LSD30CHqnZIRRKCK/BYGkXiVZUpDd2zVHEcIqu605ZbfnzvmJfVsn0Lllgox8NGHSaWrhmmAofo/N/7USw8QqjuJxqOB6jb3J7zEUSKMQ0jRmfTbdqZyoqCo+n58BWYNRFUHf1EzuenUq4y67kXten87Qk0bx+TsT0UMhTMNACwbJ+/4Ltyx5SEPTNAzDQNM0fllUyIHtYhcsWrqxKiwh34Hj8Q1XegJPOWr7UMtuWLetJopS4jjdPMeFDFmQZEddiGfnrgs73qaGZB3Qluzdyb9pAPY69S8QFwrDfwZyA8jLy+OqM0/j1Scf4rLTT2Xr2qVxjy2cPwez1mMgx+AgA5gp8XU2P542xaITEGEcR4dUQoZJfUh6mtu1ix/2CIYMgvVBTNNAD4WY+MCtNG/VikAgAWFncwspBQcg4mTIK4rKeTc/wEX/e5iktEzshRbcBKWJEyfyxdR3w84zeg6JvljWSPA3Y01dAnN+juZPGyPuYfan0x0j0rQ8yItKCnjv5WdYYilX/LJyWdznBjiguclNhx9iP2IUenVqSb99WkW/QST3PBw9O7akV6fGDUOZhLWLHuSKP2Br06kHm6rrefqntVGvqx/dwerykvDKeE2+qgvbOIs1bwV8SpPoFcWFC3jirlvCNwWNtEOwrpZZH3+wi3cbjn12/sqqhSVOyFgISPQpTlWwvYmcnBxuvfVWMjObxvm0Hz+scI3lobMre0V6/4WAFeVF3HXRGKa8+Bj3XTyW7Vs3ExNfPA6121FUldyTR4WptNi/t0rw07XNP1NuG6Dfvp4xZC2QiqJy+Mmjog8u+VQmru0pnhsDm39GURRatWnPqoVF3H7haF598iGuOPPvVbGwUZo/z9EMbgpM08QXZ33ZXbTp0FkaKVbnOLhDC9okho9tW/LS9rR3bClzEmzjUSjSVdy+fXua+VX2aZXA4tJC3nv5mSZtPgpnf9foMU/Mbri8s5eGF9TDk9W8xmZ1UG9agqpp8s1HcqP57HszuOCaW7hzoqxwaed19E3NZMSFV9E3NZPlxeG1ABRF4eA+/TFteVXTxKf6UBTJeR86dGjYGuH1kF8+YzGGafJBSXglO5t26cjVmSYlf2zHMLx5S16D2WTLznqq4iTY2Ya2bprsqA1x5YzFXD4jmr6lYDr5C7HkW3cXXhnBf5l9/B/FoiHMmjXL2flpWpDSwgLGjDgt5rGHH344/uffwPFpHpS1y583LdiLlLIi+qRmYhJeLtU2kpeWFrKwYD5Zgw/lqGGH4VMEf/y6Foht6JXP/xGhCExLxMI0DD5/ZyIX3fYg65YvomLLZtp26ETu8NEc2DeNHz5axutfxLiQkJqOiuXJUq2QSOGCPMYMP4FgMIgQglD3JhgIPQdj1O6gqHVs7izAzI/fp3nrVgSatWRA1hAUBe65aDQhTUP1+zn/5vt5/ZOZcFR8ybReXTo5dIhY9pic6KPfUD3UgcQYusGtEps2bPZrnRBWZhuAP1c2fJIRgrcvh+tilPGOgc3VsRdWfV05P30+neSMLOw6V7ZnsqlQFUGvTi3ZXhuiXRwpuaZg0qRJBINBwj3IjezNdZ21KxbDsbv5obVVbH71ah56y88Db0xncE4OJlLhK2nfVjGro/2diBVQDKgqmm7IhRfcpDrcctRLCvMIeUK9lVs2grpf3M85efzFMmnJ/lxhG+UmnVsGaPcXFLloKoYe1JHnRwS44qPFIBTZO4Tt6Y5cKq2/37sOzoxWxmkqxp17IdPefBnDMHjt0TvIPWm0255akFmzZu0SD3lvoE279mHymI3BMAyMkAYJe2H5LvsCXyDAYSeNtJJZZd9s28wftblP3r91VBROEZCSmc3VdzzAE3fdhGEY3HLj9WSlp6KFDK468zS0YJBJLwR47OU3ODKtd8zbyMvLY8ZbL8Ho3S+lbsOuvlof0kn0KR61B7fYVlA3+Wp5HK57BPRQiJL8eYy/7Fr6pGSyubre3XQj28y0xqxTSEQLoigKl//vISortyEUBdMwEIrCUSPG0b5de3KPOZ6Bg8L7WmTZ63Vba/ho8Ybw+7EsYzuivODXSh79cQ1jU/dnTOr+YdRM2+Q2zOj5JjL5XzdMlm2uZvPO2GuKI5Up9j4dwlaM+qdUdeLhPwO5AeTm5uIPBAhpQfz+QIMTZ05ODjc++AwPLrImuu67V0FtcVEevVOlkVkfMkjwKdiLw5LSQm45fxShoMb7Lz/JK5M/oUe/dDat/x06xJaOMwydw448jp++/8qZhA3DYO2yRfz42TRCQQ3FkvfqtnIZ5fN/jLqGEAKfz09SRg4rFxazsiSfPn2T6H5gFxbMm+skKCmKIqV+mvKgCQ17YA1d55M3XwQEH6lP0islw/GyhIJBpi7egt6AcTyyu8rwzC6eRKemo5lfjbkY7CqEEKiq+8nKq+dhNKJv2nHfLjGl7+KhwqKZNPMr1GrRLS+EQHFTkpt8XRutE/1NKpu725jzdpyxYoZ5YnYZVshe00wWF85nyOAchGWB/Ru4bo5X33Mz/fZtxR/ba9leJ79TvyUhZhexKfptO/2zcmRVvaDc8WY33wH+fVlT7bnQxjVSX1oIWraSFCO7GypCSnaVrd8L3tg9hCJgn5YeOUzTxNB1FhfNBzVirrU3VBtXw8KvIPn43frMlq3ayIx/wyCkaZgCfAE/IU0qFTSFP763saPSKl/c1BNmvyElyA47Z48+98BNJQxOake/8dPo0T/dQ+2JnX8Qaz60ox7bK7Y57RoMyo2GbpgutUALUlZUAOeMi3kvs2bNwohTdntXsLy8iD4pmU6iiJvEJj2utoZ8db3G20VNm2d9/gAZOUNcqpPlWLEjMYoiMA0pU9YnNZN7XptK2YL5ZAwaQkrmQBaVFOD3BwiFNHx+P0eeMoYe3XvQtcv+UZ91bmZX3vd4jGP1Cc0wPOpWJpusYiAbq+qp03QSVCVMxUIQzehbuqEKwzRREJ4kP7PB2dGuvLm3FXDktSzxgL132b2C/ygWDSAnJ0fqkF5/G+/M+LzR8GnSgOQ9/szf1qzkw9eeY2W55AHbkiumCWUL5nu8HRp5c+fgVxX23b9r3Ov5fH7GX3olN97/OKrPZ4VIFCq3bnavFQzyzbR3mHjfzRTN+jr8AkJw7Kizue3lyQgBd104iknPPMQdV5zPznVLOOGYIwkEAqiqSkJCAsPHNXHSbmyEBWzFBBNdD7G8NDxEF2wb/5m71qzjpIH9SVDVsFD2roTV99Q4tuGVhBt/+Q1uUZatv8Y8vlvvJNQYyh/x8NiPMlkioIdXTFJVH7knj3IXO2FLvf395uH48ePxRSqt2AbP5rXwwY0xz9sb92oaBm3atnOu908npkXCez+qItzohZCbnvbNA/TfrxUJPlUqAgjhbBwMXWfa47cz7sCIi753LWDi9/vpnyWTFL0ec5+q0LtTK9ruQVRgb0AI13AXG1c5/M7WsShjVmJdUsZAWrWNpWLQNLRu1x6fpdvr8/s5YvhoHnnzQyZcdxs//jDzb/ceg0VRSLA8+euXN3zwN89C2RdQ+CH88PIefW7l9gqSMnLonZIp50khUHZxgPTbVzoS0nMOxW+tA4GA3GgMHTrMU+gmQGpmdtzr5ObmhjkTdhcP3HxtmE6ypB1IQ3FpSSFTJj7D4tJCVi4qa9L1MnOP5aXJnzAgQ0rVLS0tZMZrz7G8vEhKh1rqMrL5pJHXJyWTU8+/QlIuygpZVDCf8266l1GX3MC9r04lKU1GdGJR11ol+MNkDmMZrIZherzingJjpolhmA4Fw5u3ZJgmFTUaVXUhSx0jFO5lNt3fG0QD3uNje8fIN2oihGV4RyaG/tP4z4PcCFIys0nJzJaZ//UNe132Rhb0vK8+AgSBhATueW0qfS1vsgkMyMrxeDv89EkfhF8VtGnbBjbG9rbd+tIHpGQMJClVTk5P3CnDYMVzf0D1qZia0WBYY/T5l3H2tf+jOhjiqesvctQuQlqQ9997h5dffpmZM2cya9YscnNz2d72ID5+bS8ku1wxGbZvhJkvwW8LMfUQdklMX8BPYrNmxEtTOaFTrWPgypAh9OjQfI9oArsLb8TsqFFnsX7NUr6d9g58/igceQn0zQ07fr8DD6bM/KHJ1/9jh9RK3W6EP1vqYUdaEm+y7+gmJPrUf2QCysnJ4fSzz+Ed72fv8IQ3jehKVqrqI2PYUex2T7IMcKEo1FVvt4TSGvaQ/J2wp4qEGJVbbK+cIiTNwj5GIFhclOcW/QH0kMaKsgUQGBRxDUHu8LH0TclEx1XvsPccTaUJ/ZWwqwjef3xvdv5i8muHm+ibPoiVZQuiXTdCoPr9rFpUitY2DSI3BU3EjoptnH/TvRTM/JKco06kf5os9ZuTk8PAbu32+Jl2B8mZ2Tz0xnQ++egj5n72fsMROMuNecq5l/Fby4OJ1q5pOnZUVnLfxaO5a+I0DkpOd4yUXUEzv4qqCJLTs3n+vRn8vqSIY486gpycHDTd4IX3Z1CUN4/swYfSp+fBca+Tk5PDoYcfHaV4tKvQznqGKROfYuyEa+jRPw0d0EImK8oKufHckWhB6cVNOvRYSLmg0esd3D+N5Ay5di4sLuDqs0agBYNMnejn0Tc/pHdKJj5FQROG0342ce/nxSXcZH9mwM9tL02mb2pWmCZ7JGzqhvfvSIRM05F6001XUHb5pmq21QSdse1VvjJNQUVtkBpNp98+rTxSbsIxous1nSUb4lcztGletsfXi7fGpfDJ4o0xz/v0vExmrt7CM3PWhb2eoCrcfVhn6jSD5gG3aMq/Cf95kBuBrUHaFM5iIEYJ013GsdeAoqJpQRYX5jkZqaYpZbvueXUqYy+9kYffmE7f1CwWlxSSN+v72Pe+tjgsE76qosIJgxm6ztGnjuPY0WdJjWTnOCGl4fqncu19j3PhjXehCMGq8hKK4yRR2AlKOTk5/LasDPHTG40/Z4cmrHBt9oERd8OV0wDZDqqqcs6N99KuY5xiAsBbj97F6kXFTqa+zbv8J7ynAkGLgGXgCDh8+GjZ3sEaWFcafvDvS/hl5RKMGAZjo/g5vFhI2w6dHK+GYnk6/L5/Lkt43BlnOXOqb/arZPg9xWvMaJPg3FvuZ8R5l6F8eOfufaAQTvGHrBwpoxRZLvefhBDQPKBG62TjFguJXISEgJTswWHeeNXvp3+6W9JcbP1VPncggcNPGe3wBm2FgH+TB11YlsQhHVqQmpHJiAuvpE9qJsnZg6OObdd5P448ZSx6SN+jVXTB/Dm88eidlOXN4dVH7mDt4hKH6/1PQQBJqVkcdeIpHHHSaLoc1LOBgxW69exLy1at2ffAOAVGduGTQ5rG0uI8FIRVcXL32kIIaejffcftYV741MyBnHv5tZx45DAObNtwQujgI47brc+OxJLeY7j7usv44aP3MAyT2pBO2YK5bknskMammsYVkJSvnyApI8dZQ+xkSjuCu6RoPj5VkKAKZ0OrgDNslxTledQ9NJYV5zvXshP8YsHfyCDVdAPDkHSRqvoQa7dJV9HG6iC3fLnckbYDT3IibiTaG5UGN9Hvhfm/8OGiP+O3h7ALxQhO6b+P8/r+rRPwKQ3zh7MOiN589ujQnE7N5Vxmbyr+bR7k/wzkRpBxQFvSu7Zhv9aNZ3vvFR3NfkfAybdAi7YkZQzCMOWAMDGp1w16p2Ry6gVXSiUJAcV5c+Nyt8Sn97OsWGZlKwIycoag+lRpNKkqhyQNoNN+XTlt/ARUVXWMigtve4CH3/2CkWeei0BKpC0rzgsbAIqqMn78eMBVscjLy+PdV57FDDahAlSH6AIhcaG40miGaZBf1TKccxmBUEhjuT0Z4VZ/+ieQ4BM8cXISVx3a3Zlg4sH38V0MPPJE1AYKv8SDujicGnPYSaOccJhN82hsEvsrkZOTQ9t2Uo7w8ovOp2u37u6bkbdUvZXq7ZX0Tc3iovFn7Jb+bbsWiZx++U3c8coUJzwqGliU/m74VSUu5UcIQfOApFRECn+kZQ3kkbc+4vgxZ3Pc6PH875WpJKVl8uDxfXh19ADuyu3KyIuv566JU+jj0cH2WZv3f4sHHVzng/wdJwHo/9o77/g4ivP/f2a23KlLLrIsyQVXFasXW7YhMqal4ZgYbEogBOKQBqkkIaEFQglJvtTwiyEkEAKmhRIIAWIwYCNjS264AgYSN4ybLFmWdHe78/tjdrZIp+qT7k6ed17Eur273dm52ZlnnlpQ2tmVrebMr2DuvPOg6lrPAZ7d8N6qtxBsb7eFlvfWvGP3TbQgBNi6fg1uvmIhXn/2Mezb+V8oqqXhf+7mTh/+3wdb8di9t+OVx/983BdWNQ2FVjVSMVf0VUi2vDM6jSxNoZiamezyt+/+vJPyC7t9v9cMy4F51g/w8L13YJtVlbWoeqbtWkOLzsSemiu6P8eRTzE3f6yV+o4gUVNx/tlnuaouaiidPhuawvtLBHyLqpWEEFRYbifCnaewosYORlMowcThSbbyREAJPMf+EUZgDZlAW8iAYTL89o0d+Pd2J5NNa9D0FPwQrhgi/VvQMPHe3iYnlzJgp5bd2dj9uk0Is+fQCcMTce98nn+dKw+7/23DPWImc85HwC2cme6YhBgg+na2OKC3E8aHm9Z3/u6bD4F97ht9u+DEaujpIzClpBJgDBvqV2NLQx3GTqvGtIoqS8ghABgqamaDbngprFmOKgoKK2YiscNDyMB9M++/5ZcwQgYItfwarUjf5sbDltaJQqEmKCEoqKiB5tMRDFjRuVdfi5qaGrvMbiAQgK7ryMoZA9D++yL1BDNNbFa71z5TqmBaZY39yJIoqs00RUF2mh9JugICrlUwQpaGuEOzrvvTU5hSUgFNIbi/a0tXWGpOrsUK12vhVyg058REr/KRDhQKJfhiUQ7+vnY3ppWUYfnDd7re9UrIykOLUfjA06AEGD8lD8qaNvRFpz7SaMSMwA5Mm16DSUUVtumuPybkgaJ4dGqX8wolVmGbDgNEmG8Ly6sxpaQSjDEcDRgghJdAD5l8Az1xWjkSfaqtEeSuJUBagoaTXNUQo407PkD8Lppl7ajITUPDLsel7dOdH4MUnYSbH3wKS9bswYfHeWFKqS3kRNu0m5eZgvvW8IpspmkABJiYX4QPNq0HPuroZGRtchmDYfSsBe2OpPRh+NrPb8bU0ioYJnNZm/qOEJI7kuxTe/3MRXR6Gj0V5qLfYXN9HfJLKzE2vwy/uv8JbGmow9qEArzf04RCKGrP5ukGx2YkYHiiDpo1E/f+/VmsfmcFUjMy8OpzT+CV557A5+cvxKTiCq7Zte5BIUBJRTXue+xZvLvybUwonY6TCssBEOgqQZCQsBlkCAESXKkzV35yuNNnmtuCMEw/AgbD9s86Kw8M08nYIazPbs2s4fJfFm4YrUGzx/43mcvNAgTDk3T86rTJyErRQSn3Qf7gYEsnNw0eKNj55Iy5rHoEGJGkwx/l7EIdkRrkCLKp4d1Ox9j2FWE+2TPtUPHq049i2/p6/PwbC/DQ/92Gm69YiG0b6u2oUwAorqjGrFPPCHuO2i8vxJSSCmQkaJgwPBHrVq1EKBjik2soZJuLDMunkVDKd7pVM0GsvLG6QrFl3RpsaajDN67+NS6+8ue467EX8MVzFoIQ4imzGwgEMHbCZKAtQhWv3PzoBUBRe71ZIQT2PVBET3PIi1M47ivFVTOh6RrPGdphVZ5aWglCeAGDcH653bHyjVftv6+u5b5+4pbtQKgoCoeUEFxUnoO/LirF7m3roSe4kuN36IdT5y1CXmkltm2ox7WXLYDRx+j2A8sfw0t/vBnXf/M8vL+xwdkogU/UvSlIMNB0NY4JHL1qx48MT/TZLhMi3aLQvArfwA82NOCFv9yL9zfUw53BRVgvolUtLhyO+5P1rAKWRg649nSXm8GGl7H2sTtxzaXngACYkN/PYOg/nA0AOOfSb+MbV/0c//e3Z1FUUdWlcDdYJPtUlFY71j1FUXDGggu5K1ZHXA0lx6FJB3jF0od+ex3e31hv94FPJUjQ+nZe3cotnpsWvuBFkq4i2dc/wWfYwR6CFrsjZQTyK3h6x60b6rGloQ7jS2pwKNjz/Q0fmWmnR6TECV4srqxG2YxZuPvXv8Dzj/0VLzz2V1x54Vewbf0a280CAPxWHxZXVONr3/kh8ksq8eHGBjz1wN3Ytr6+y/GWqKlI1rvvq2te3o4tnx3lWSjCnIcH9wPuMtRwCcPOvyJrMro8lxvTZJ3mnS/kZSIjUQcBQbJPxU8+19nPvLE1GHbTxayNO8Dnvaa2489iEmmkgBxByqtdgTLHGo/zbASr/vMvbKp35z0NYtOaOmzfUI+lf7oLW9dxv9OP2sLnMuVZDHjqFJ+qoGhCrpOw3I2lOaaU4vKrbwIhwLMP3oP3GlZj09o1+NHF5+DJP96Bh357HYqrZ2JaeZX9gNfW1tpZLHRdx6w5p4GGWo/z3rvAl9yzi8BD34JpGtjSsMp5+KK4+PHMBNQWVPLLKnHHX59B2YxTEM7HlAAorpoJ5b6FfboOszY5Kc07kXLoQ3sCE0KSapkAo6Uoo4Tn+Pxk81rccsUiNCx/DVRRMGzUaJSfcprns587m2ff2LSmDkYwiL6mj2cMjt9ffZ1HU1kxJh1qhDKUDBSEOL+bO6PpScMTXSZJZ9MlXGk+3LAWN1+xEM/8v9/zzcGGeu5eQ2LHtcSNHdBEib0QieBaj8Zp2f0A41XJXn/hSZTldPbb7i15ZdPxjR9fiwu//QMUx4BwLBDBtIwxGCED+3f9F0CYjZT1mlCKmWee3f8L7t0OvPsU96VdU+dYm9D78vGCCcMSUZKdiqwu3BDzR6X0qqDQjq2bOx079Ogv+9SWjkwuKcf29fW4/vLz8OQf78Bt7+7HAdrz+KGq7nkOBQTAhndXIhR0tPehYAAbV7/jfB7cXUA8q1vWrcEDv/kFblx8Lv529+246qL52LZxfdjrFmSl9CggA8CHB1pgmCzsb8VgVcSzXogsFk5WC+aptmfF8fXofrWnqd2+P8GOgy0e95qOVsq/X1AG1XJDOXWSN/uMYSXoF5lAIll8JFLE9koRZ0wrc/nOhaxk2/2dfQlB9dzPo6CiBqqm2SmQNgbSccN7FA/f9zv84hsLsKH+Xew+Et53aGppJRRKcMTKlXvo0EGnel5HLCH5420bccM3z8Nj9/4W371gPl559glPoMF7a97h5mrra+4yu8uWLYPfaAFr7aN/QG9RVOBLP+v2I6RpH1SVp7cSGSzExB8NFEKgWJEbYgItLK/CJVf+1PEztDBN7pOVX1aF077SNwFZ3F3zkSbc9K2FeH9Dg8elQFdplHqAw7UwwNaGOtuUbBoGDn32KTa+663eOL6wDAQ8IE3RtD7bvxXVSeNVPmOW5ROIsAFxsYhYcML9XgTc58+tfQW4kLnF1behYBDvWZsD1dqgxdryI34XIaQKDbLItxqO/zz3BJIOfIirC/p3N7kTpkCj1BIsXD6j/b2JCLFx9TswglyDZpoGnnrojwiFQp0VAoRCUVVc8avbkF8+I8yZeof+r9tB25uhilSAcMZSXwVkSklENp3b31vX+aAR4hl/+smOA8ewqb7OtpYitevgbjctLS34x4P34ION3uBnSgjKZsz2BMqqmo7S6bP5HCfiPRQ+pkTmi389+YjdhkAggI1r13R57Y4ukWFhXDcc7qdiYAgaQkNs+SHDCdgT5bbdbhj83rq/pF+j3EfdtYly+1zz+/aOg2SfSC0LLJ4xDhW5ac59atQVIxHtJzA80gc5gqjuaGgxsfUzAIQoKvbt/ASnffUiXLfkCWxtWAU9KRV/OWwlF1f9CLYfxYq6dwGUdXkejTrmIa7t9SEQaAczTVBKeYUfyxmJP/TEU1mKAdB0DUErtRwXOLxyf01NjSdy+ebf3YXeF07tA9/sRXYMxnDGokuRX1ppL3u6Qj2auMFERDZT6gTBKJSgqLwK87/xPTzdoaq06NuT8ouAPuwz8otKIAqhB4MBbGmoQ+X06baWTlNoVDVlwtUl39rwBQOm40sZ6uhLyaW/grIq3PLQM/jlmjb01tvyzCkjUFF4Gf67YRqKqmpQVsUzPFDCNwnxgL2pC/N7EULgUwmOBYjrs/y9aZU1+IemIRTiz6rIBsH9mWOPkuxUfLq9nZewBQEjjC+UDGh33fzUojJbeDIMA/98+H40rH4HuOLRrk4dFlF2W1c7p9mKtha5vGY2/nI3hRFyShJTSj2+90W+I5g6qwjTf/ItTC4qx38P999Sd9GVP0db4wGUTp+FScUVvMqkcMmJUl8UlJQB6zua2Rnw/goAV/frnNf+ezsuT8sIbznthmNHm/H4g7+FqmmoHPMKzpxzCgA+ZtvLq/D/nngBLzy1FJQAXzhnEaaWVqI9ZNr9JzLRrK3jmS/cGx1mmkhNS+/y2km90LYL/+FwmxnTdGmJ3f/Z32WO0CxeW2Wku+LP5xUjwZWKTVixKGWeOajjPslRUDmvAWDu5BFYUDQaJNQUc65fbuJjxYgTVNcAq8mwRMTWJuD+r/X5XIwxvPjI/8Pf7/oNphRXoqCyBm++sNQRvC3/s5yirpOvv7+hHrpC7YeopqYGdz/6LL75o2vwi1v/gC8vuhiUKmAmH/EVs+YgKSWFmzwpharp+Pw5i3DXo8/hgu9ejZseeArV02t4EMnwpLDXrKmpwa33P9zn+40UjDH869EleH9Dg+2X6e+jT12kEbtsWJOmYvm0ZY+b0Olzwlz+8bb3+nSNMeOtcxFe0jQlPQMJmgK/qtiBN7yUZ2Tuqa8IrcPUkkpct+QJnHXuRU7hmtZG72eJ182gCF2nHurIF9IOobisEguslGFCMzEsiiWV+4KIcicEyEjQO7Xb0ntCpZa/rqvcbV5pJW548EksuOIn+M2DT6GwtIoXyNH5HBCubHo0cfKqujXGPO+ze63+1i9ugu73gyoKFEVBw9uvwQy0hztl1+xYjUt//htMLa3kBYQgNPC8rHJeZkqE7qp/VE2fgSt+eSt/JiiF7vPh5NO8ac8q2rbi3Mu+C0qAp/98D458tAlPXVLRr+t9uPU9nGNlQyLgcQoi3V20ZJUzZ1fjS7nei08tnd7Fp3vP+pVv8D/mLO79lw7tst0aV7z1ln1YtdbTkorp+OnNv8fPfvMHniUHzpwF6++RST58rpZX43ULn5RSNB9p7PLSei+08ULcD/9buTTHlvDrFBNxuVxYxwwGfLC/Bds+61ojMyrZx1MB2psoJ+Wb2+0iXCA4Aay1z9n0f7lgFFITVPu1L8oWzq6QGuQI4n4I7rnyAtz1+9/ib4E2AP3c6RecipcDJ+Ht00px5OB+Xlo0KV1cDAzAph07AWSF/frmhlWYXlPjeYiKK6pRWF4JXaE4dM8fYIRCYMwEM4C6ZS/b51YUBT++/hYUV/BAhdz8UjArLQslpFttnB7lvKKmYWJT/Ts8FR4INEqjJxgKYc/6W7UCBwkjGJvh+OyNsXKEip05YX3rw1dfeAY4r9K+UHPjYR4gqBAYLPr5fy2LNgiAvJJKJGgK/vPcE7zoxbEmqH9chNB3lgIAPtjYgJKKamxfvwa/vOxchAJBKMVnwDj12z1eZ8Xbb2FBXpl9v4QA2Wn+XqVpjAUyk31I9qnYsq8ZJw1L7FTZTNyXqlCrVKxYgPibeSWVGF9YhlQfX3y45YKgICsl9gRkyueRkGnwxZM5wYYUBL85Kw+fNrdh5Nh03PTAU/hw/Sp8umcXXlz6N4D2QSO48z3kbHwCc69/3RWXIEzD3KSd7IvuUqhRgs+fdxHGT8nH+ndX4uRTTsH/PtiG5a+8BBw7AiSmgSak4ION9bjpWwsRaOfZhL75y1sA5PX5em//8ymcefZXUVpZDQIGTaHwqQoYY8hOi86zQgnF9z5fjRcfcILdZ31hPt7fsOa47H+H9n/K/yj7Uu++sOFlYOXf7KD1mtkne9625xa453aCZF1Fc3sI1gyORF3B7Fk888WLzyzFv55+DKZhQNV0lHRTVbA33ipCIx1Og7xl31GU56YjaDIYliBMmQjXI7b2WJwnYJi4/pX3u78gEensYAnKACMAYU4cgWjKvMJReH7zPtdX+edNAN+uGQ9d+R8qx6Thf42OXKRSEpNaZCkgRxAxQGrGpSNkmCiqnAlFUWAY/YjOJBQ46wcwAC4cA0DVV13vK2CmiXfWbQImhRGQ21tQOL0GftUrHBICPrIBlM/geRoD7W1eXzfLBHPk8CHbTSFJV9AaNDzmlHDU1dXhx18/D7ji732/5wih6hqKq2dBVN6jJHrBaWIhFmYmvlMmoIRh/LAkPHx+KVraQ7ySEGCbm+bOOxf/Xtv7VrNGa0LasQZgDKnpGfbkTS0JKqouFnCCQAgldro7xhhMwwAzWoG3/wrS2oStgQqUVlRj4xqntDp6mckiJX2YrYXlQzr2Jt3uoJR0mxrL9v0D70+/qiBgiCpe1qIp3iWEWytcmtpYQ7XaRglgis2hJSRPzUzCuGEJYIyhoKwSM2pqsGntarzyjycQCAR4phfaC39NQvH5Cy63/hYb1Oi7VXSEEILCskpMLCrHqGQf3lv9DneB+9tVwLAcHD37C9jSsAqB9gAYM2EYJh645Rrgyn/0+VosFMRb/3wK5ZVcQ5uoKVApQchkyOkiG8VAE+73eOSO6zDv69/Gc8dx3slfugwfbf5+7z78n/uBjVxRRBQFV/z8JlRN71il0vnXjgEAF/Io8X4GAIoqqpFXWokzvnIeNtfXoWT6LEw8aXyXTeitP+67/2vE/pbOzoy3LPsQT19cgdagAdNkUBViV8xjLu2x0CYHQ73bbFLC4xkY+MbcgGPtEqG1IAQLy7JxXkk2PjrUgiNtQfg1xV6HRqf68IezCy3XQ8KfeWFhjUFiS6UwBHj64gpcUTMObSET+WWVuPyaW/o3E/f0HYUvDGbSsE5vpZot+HFJIvJKK6GESQUkdr7FFbw86PwLvg5N99kBfIRSaJqOiprZLnN374oNLF++HMH2XhQKGSCq5pyF65dY5mXXYhtN3FpkSoSZik+mqT4V6YkaEjW+V6WEC9H5ZZVIbT/Y+4scPQD88UJgzTMAgObGw3CEQyctT7R8sQEnNRkFUDFjFjQrcb+iadA0DXTt89A+WIGCipkghKCq5mT7M/SjNcBnHwNb3uj6Aru34NHf34D3Nza4Ao5ir3xpb0jW1W7HrVicEzRq/y1+Y2HKdLtrxCp2gQUQ7ittCxjE1koR4gQBFVVU48YHn8QZCy4EvWcBsGppj9eYMi4bpy+4yBa+AdfCHiPjgz8XvB80674ramZD13XQtiPQP/sABZU1PPjYpWI0zb751tpYPrmEAH6rXHS0FXjhxmkoGEDr0abjOu8rwXE8TWgP6A9+3RaOAe4r3Nrc2Gl8uGdVAp7eTmxcVUqhWW4YIphdzP9FFdX4xvd+hKLyqm7b0ZvCNSYD7l35Sbfvh0yGtpDJK+cJtws4AXuAlRKuF+PfPa8olgXZuTfiuU8KgqxUn51z3XatIAQHWgL4+NAxtLuEcvcmI9aQGuQIk+xT0eb68c849yK8/a9/2AFUHZk53MQ7B8M8EO7ZInMiMLbE+z7lP50yLKfTV08uPAnTK8d0Oo147d4BF1dWo7SqGmedsxCr33kbSakZaD5yGDNmnYziimrHREN4cFBPAmdtbS00TR2YIL1eMKmw1C6SAQCKtShGq4Kc2Dl3nkT4RMN348RO8+RTFLs601UVw3DTc6uASV1Eq3+yFhhfzq9DKVhbMwBA03UUWpHpjvAUZQ0ycXzVCIBp5dX4/q9uwVuv/BMz5n4BORPzsO7dlZhWVYOJVnGPkspq3PHwM1i9cgX05DQ8+vufIRQKwpxQBfiTO1/kiZ8jpCjYUl/HTceExaxmoifyRoX3iXXMu0QobOBTFYRME4TwSBwhLAuXChIjQmA47PFJuGuW2+ohxi/gck0CbAHjjeefgPnOYyCBVrBTLg17/lNHmbhkzhxbELaFmw7njzaiLYwBuqqAEoLiimr84ZF/4IWnHseBzz7DihefwaTCIsyoPR11b7wKxhi3APb1YtvfBiHAnLPPBWBlRYHoi+h1iBDCbP7QizR2u7cAOQURub7R0gRFVe2866qmoXLmyZ0+514/K8akA4AdMLl53RqsW7US1TNn46TTawHwsbx5bT0aVq3AjFkno6CsCnmZYeYvi964Gjy9ofu4DOFQYYKhLWjavsFuLTIhBO1WtoveoLieGV2hOEZMZ9yIzTj4b7i3qc2y4DLHbZDwa4ZMEx8fOsZdeoQPc4w8hx2RAnIEsbUTLnn3/Q31eP+9tcDnwn9nWGYWcPCzMCdzneSi/+v8vqJx5//0LKCVD/Dv1IzD4dYgTs8b4Rlw7uHPfYGIM2ABJGgqiiuqUVBWiWMBE7rKTbcAbP9G99zZXRqgmpoa/Gnpc7j0rQ5BNP+5HzitZx/S44H8bz0Kv1zj2dUK8230XCwA273BNbESZuV+NJ0PEsbg1yjX5oCgpLwShVsPYnNXNUMO77YF5K9e+m0cbmwCIcCZXzkPJ1lCJuD8XpNGJPcqx+ZA4Gg3+W+zed0a3HPzNQgGgtiwpg43PfgU5l/2fSiEaz7EIjGtrApj88vQEjQwfnI+3nrxaWxedhv2zv4+kDbKcw2RCpGn+CMwukiDFM94TLhMCMgEZoBbJkwrGl1YKtzJ+GMTxwVJU1xaKTjmTVGaF+BjJ1lXsXmNqyJlw3OYUliE94d3LlHdtP51vLxzFYqqZ2LitHK7aBCz+07pdxGLSCN+P3dMMSEEy55/AqEAF4P/8ww/pmoaTp23EKeefR5+tq5vsxv99x9w2S9uQUFZFYRfKSB+iWhamDpvZ6miYGJ+cdcZfVqPT7vshoGhdt75PAMFgLnzzkNJRXWnHpk0IhmfHDrmydtLCE/p9sOvzUcwEMQj9+kY/c+XccrsWXivYQ2uvGg+goEAHr7397j3789izIyiLtuh9iKGp93o3nLAGAMzrY0xAYIms4qBEDu7BcBwLGj0avMsrFHW3UJx+Rw7R70KITGeqThmX8dRtIkNMhA7m1U30sUigojdk/sxX/7CU56k4pHivEu/hfuXvoBDLvXBiKaPMG9aFhJV1TNYvW10mxn5oC/M4toqkS/VbZZ1+y+SDt/virLKzpHHs8qnAbs7J4K32dG5CmFfGN/6Ca7/YgnXHsOJylYpQU5aAlKjGIQjFnti/0+4rBC7v500OcROEUQowXfn1WI87WoRcH6IZx/+EwiA2i8vQH5ZpaM1dn0mza9GrUiGPSFaf69/d6WdXzsYDGJT/TsQFdUAQBNjkRJs21CP5x+6F/VvvoI3nn8ce7e/BzQf6HSNBVf8GNf+6Qnkl1Z6SuZG060k0rjdnOzqeGK8WJ+hBEj1i/HuCMuxiBgP9hzjmptgj2GX9sr60rSqGlBrE88Yw4cb14Y9/9q3/oOl992OGy5fgPc3NMBnfUc8H0WjU5Hq18J+dzBxCwrulIw8P7J3/WBWJdSsnDHIL6vEoxeU9elatfMW4YwFF7kEUmeeiOYwKctJ81xfUVUwxvDgb6/t+kuv3Qtse6vr9/uAqmo45UsL8K1f3YZvXXc78korw1of0xO0TlkXKAEa6la4agYE8M6Kt5DiV7F3S72dAzkYaMeDd92O+npvfmVPOyLg6yI0yO7cx21BntiNB+nxexIZLXpCbKTcFh9nXXPNR+K4a7Pjtpraf1PXfHXcdztwxHLb4g7HnM3Ztr4ey55/okv75ld9O7o+ma9rEwwATJ5Witz8MgQM59y3fed8fLCxgbeFOhOe+wEvyXaKJeSkJaDAEo7FAkrtAc4X2amZyR6hDujZCLexYXWnY7PO+Tp+fVY3prD3V/Zw1u6pqJ6BQitlEYizw1UowehUf/QEQ9fEIbTZwt9Pdf1G1PrD8Z3lvropPhWXFqaAPHVNmJM7v4QRCuE/z/wNv168EFvX13vKa4sJLdpQa3akBCibPguaVYFR06ygSjgTkq7yBm9ZV4/rvnkenvrjHXjhr3+0zJ/hn6ezL/0eppZUco0j4abjaPtVRpqS7FSk+lWPBkaMcycg1NnIEus7sZwDmrjmnuzUBPuZcZtsudDoHM8vrcLceQvtgc1C4R0NzJbDAGMIBgJ466WnufsSvObxWMC7OXDmgcqaWbxYTsfPU4qKGv7M+DUF86eFz2QUjtlfXAACPv+o1Ds/RHMj5d7UAgAzGZhpdq9gam0C1r903NfOMJtxzf9byhUs1Bkj3Vkf3V2VmexDRQ0PeqdWVdnPnz4XAFBba6V6oxSmaWL1ijexcOFC1NXVhT2v0s/aCW5EHmQI72PGEDKd3Mghw0oBxxie3ti7NJp8c05c//POQfa8Q53nFtY3hBnaUZQQW0ZR7Q1h7E3WsTtrxinugfFefR3MUBf28X0f4vnf/QzYsSr8+5qv2+ts2bQRe9wV9ALHEAoFsbWhzuPrmaSrGJnsnEsMRl2lGJ3qR4LmuFK4F9pk63vJPhUVY9IhKk8BXGjrjvq6FZ2OrV/5OrY0hJ8QAIAe3tPtOXuiqS3k0aBxvzonuCdaiAlBpTz3MQNQnptum43FeHHa6dqoWIf++fD9YDs3hTt7pyPBYADvramDX3PMxo7mNnp94c4PChBMK6/CPX9/Ft+7+lf4wyPPorC0yjOxiuIWG95diVAg2DnR/2v3As08u4umENz9lUII050QLlQldn3b+ou7H51FxckEAThlbsXnYrm0tv17ES6cjUrxwadSj7BE4ORJdWuw5px9LnTdx11rPlyJ2SO9okzVnleARmfxJ8yy2oBgXEbiYN1ir2DMcSuihCDVryHFr6K0cjruePhZVNSeCaoofPOvqvjxjb9FcXm1LVifV5qNM0f3HLBXoBzC5OIKEMrzYlPqFneiv5EW4ztJZU4F2TAbBABAy6GIXHNaBsH3i5Oxpb4OH2xo8GzUhyXqGJbY+fq5aQmeMTQ2IxG1J8/G3X9/Fot/dA2WLVuG2bN4oZ6ZM2fivseeRdWsz/FYEdNEMBjE8uXLw7YnEllSL/z7OhxpdTaNpqdyHk//1h4ywRh6JSA3tgbtCn9ua6DbOkWFRRSOxRlwFEOioq0tXFvvJ1kZnGIR6YMcQdxaGwAorpoJVdcQCjqJvZ0PU4SCAfzrjh8DCRnA4l5UiXMxqaAYe5q4gKw9cTVCn30EVdVQWFljpVDhO99xGQlI7FCZJ1zkNqW8Ao8YvCcNT7TNkYD1QDCeMzRvVAr27Gnusm1VM08GlntzP7/65F95UNkPnw/7nUsuvRTDT2L43bb+PSoMXGvOGJCgKi6/uthAIdST07YgKwWr/3u4kybQO6nw8XRY5PFsbQISHAvAhCl5+Kj5IJDi1LgXRUKSrKpHqkIweUQytu4boPLffYASPvkLAae4ohrfPOdMvP3RQRwLGJYW3THZASIVoYZAgGuTbA7vBp6/Bbjo/zB1ZDIyk30wmPC3JbbARUB6VZkq3hDPMCV88VMpRZAwEMKchQzxsUGwBWFXWxXXptHRUBF7U0AA5JdW4YYHn8SmNXWYVFKNoopKrHikwT7H508/DeueeQBGKAhF0/C5Ly+AqvBgY7fSIFZQhRWAALlpfiTqKhpbg2gsr8KP//AgPn5vLXZsWI2KGbNQVjXdSe8H4IMNDUj7bx2G1W/BoZnfDH+BtmZ8+Ocr8EHe4yitmg7F2rC7+z3aFheVAtcVA5vWrELGT25AW/MRDB82DPff+20EL77f+eDO94CX7uB/H2ebD366B7fdfBVCwSCe0zTc+OCTKCitAgEXhMNZX1L8KlLCiE8lFdUoqajG9HEZ3uOV1fjmD36GjfWrEAwEoGkaamtrw7YnXLGN/rCnqR0ThyfyoDyInMjMdr/orXsFAAQNE36VImgwe5MK16acMSfNnUmc+Yk/u0IoZq71jdhWTrc/c6wx6CvH9u3bsXDhQvv1Rx99hF//+tf4wQ9+MNhNiTgTRyRh3a5Ge3AUlFXiip/fhJWvvYSGLr5jGgbAuorC6hqDwRaQ777rTry5fDmmlE1HXlmVVRkPLqd4Lyk+rVPRADGA3Ts9z/vW8d48T2WV1cDyNzscDSOVuzh65DDaNtYB+syeLxAGKxYBEA+crU2M/pPHU7cRj4CcoCl2g6nV70JDBrjNjQTTqmZix+b1wJ++Dnz3MUDjifwPHzoI+sIvYbp+K0IpjjYe9pi8EnUVk0aEr3w4mBDCs3S4XwOOdlsIxpQ6QlFRRRXuePgZvPP222hracbzD9/vCMqf7UClug+XzpwGSggPmvG4qQClOWkxmYD+eEjUFW4xsTcTPLgzaJigxMlIEKuZK9wQEPg1ipDBPFolsWh6Yx+csrbOvbsE7A7nnlxcgRseeApb165C6fSZGF9YHhNWpa5QKYVPYZ7fTiGOy0xeWRVmzZwJSr0uKNs31OPX3zoXoWAQJKewmysQ28pYbpVgd885AJCVEt2COlvXN+DWxQsQDAahahou//nNWPHaSwgd2gsE2+y5Dwc+AW0/CigKqKqjH5UGbNoDAYSClu9wCNhcX4dpZTxTSqSGCgFBcWU1/v7sS9i+dhUKCwtRU1MT9rO9CdLrDTe8+j4uqx6DOZNGgBHHH1lksujrBOGer7nVSgQCAwBDgqqgLeTKbGGtyo4CiHjORSyrNCXA8ERfWE19tBl0AXnq1KlYv349AMAwDOTk5GD+/PmD3YwBIT1BQ4KuAq38cd22vh5/uu1aBNoDIImFYEVnOh8mFIrKu98w+y4g3337TZh52TUYlqihqno6hk8sQnuIF/JghAd6GSx8ffWpYVLMJOoKjrYzl+nWCyV2fZH+0cNMk19RA59KgQ39PD+DnbzcrT2OhbWQ75KpXcpV4A5q4IKAk5aMB6lxYTEpJRWEEDAzBBghwJpHDh88ALS1uK5DoOk6iqpnQmx5RIR+WkL0J5/8zBSsbjts+c17gzioawJ1790oISitnI7MySXwaxTTTz0Tz/3ljziw71PUfmURTvvq5+1zuDWNduL6IUhOWgKyrcqAq//X6AiKBNBpZ81rLEMIH+uGaXo2s9zaYI0Rz3EnaHj7+jW4/pvnIRTgwtQNDz5pf6524nAQwstvTyuvgqoQtIViO6uJe0MQ7hiB109XCM7LX3jaznLBultLXvgNVFVDfkUNV6CIDbolPKUnaMhNj06REMGrzy5F0LqXUCCAJb/5BfdF7uhiRbiNdO78C+CbVIUXjyOvqKb7oKgKWNCEoigorprZ702U2wropiwnDQ27GlFRNR3nnDkHe/Z07VIYrnZBf/nz6p3Y1diKS6rHwATPEBSyFAxH2kK4ZdkHvT6X2KRSa61K1hUEDO62QSixNciMOJYtEYzH1zqn+p83tRtBWoIaE2tUR6Jqe1y2bBkmTpyIcePGRbMZEUWkNAGAjWvesase4bX7gNRMYFwZACB1+Ej87KF/4PXnn8Syl57r83UMk+GjfYcxOjOXX9ejjRPCRu93iJNHJGHf0XZ8fPAY12B0+C6flnt3vvX1nYP0aufMwVv/XdvZ1QQAfedRoPB8TC2tAjZ0Hd3bHQyO1tyO9I+BxVAR5iVrMXNrMx3Bhveu6jI1UcJ9BJvbCQora6DpPgQD7d7fxTWRarqOU+ctxGnzzkNBKc9iMWF4IjIS9UG5z96Q5FM9E6M7bZDQDDLwBUL0F8CcCRa8lPI1d/0FbSGT5/K0z2CdE3yzZ5qOVn4o4tHGwAlIpS5hyraqxDjitxdBc35NQXvItPMiuy0CZTlpWLOzEQoh2LSmzq60GAoBm9fU4YlLv4eQJWwbJnNJLLx6ZSxYlLrC1ox3aKJbSLYrKVr3xJVwrjnhs4+QFDiCFj3Nc46T/vc6pn/ly5hWVYNxBWVw9PI8TqUlcDw62MjR0dpjGlzgJ5ZZyb0CMcYwcnQOPmtptpUG/SEpORWiJJNwC3AsqX2jK2MVpaTX61GkNMiCV94/gK9VjgEzeVnp9hCDQoA3PzqIHQeP9fj9X5w6yTNnTxudgnW7mqBQCmKaVuA5AKvPTAJQqyCJcK7QFMIz/hPi6l9epyCcy2esENXojaVLl+L888+PZhMijnsHWVw101P1yD0KktOHI6+0Eid/aQE0re/7FEJVBHzpyE7z2RcW1xZCOu3qaQ0DpaRTuig3PFCvdyJyfd0KkCd+Yb8+D+vw3e9fhVv++mznD4fagTXPYGtDXb/935JU4EsFo+zFw53XM9rWdUqJ3XdhFz6X8CzS7In3VEvgmVpahRsffBKnLbgI5NP3XSfg/5w0eSpu/+s/8O3rbkd+WZU9KcUihDhBGaU5fBEXk6W4d9UdoCWEA5fw4O5HuwAJde5as9QWsSwMRQrH3YDYGld3H8VLF7jbOWFYIsZmJHiEffEZSh0NcnE1j/Hw5L/ucO/UNQ7ETByLa3FGombPV3mZKUiyAqGFGCv6wQl4cjLizJ13HlRdByEEKmH4RWVqp/NPm1GLcy77PvKsIkpugSd/VAomj0hGbnp03SsA4NzzL7TvRVFUT1YITXdt9inlBZEqazAhb9pxXbOxqQmhYJCnPzMNvLemzgmi7sMDlOzjBV7GdxEAmpnsw/CknhUWEZaPAcD2PQ6GTATFpqOX3+WZr5yNmc8KAiaWYKxSx63RtopaHxDzEU+56mTJshVDNLbn6ahpkAOBAF544QXceuutYd9fsmQJlixZAgD49NNPuzVJDBb79+/v8TNNB5vR2twO6lMwdux4fO/qa3H3bTdyv0mXgGwaIbQd3o9x48bjp7fcjVs+7mNjVA0H200M0ww0HtiH1qZ2BAwTVKMAAwxVARiwb2+o1ymeDh8LovlIGxQK7KPHOuVjbDrYjHZVwR6zudu+yCsohHbgdrvCU9nUiWhr3I/x48cD6z7p8GkKVVUxKa8ArYd77t9wPHT2BLQFj6K18RhCBkNzu442hSBkMqiUYI/a2vNJ+klvxgQANB1sgkJ4Lsk9yjH72LGAAYOZCIBCVYCmVp7CyzCBgGGgvTUISgjGjh2HS773E5RvXI9H1m3H3vSpAAhUVcUPr7kB2ePGo/XwATtKn/pVfGb60TpIOV572w9HDx6BrlKYjGGfj/vQNx1sRsgwcSxo8KARTUFTq8hxa5VMbQnAULgPPKUEbVaCe5ERKUgIQgYDUwmOBHwImSbaKcVe9dig+pz2th8iRdPBZhgmX6gCVvEAhVAolI+hzGQde/YM3Pjvjt70xZEDzVAoEDQY9vic4kJNbUEcPXQMbW1BUAKElCQkaAr27GlF08FmBIIGxo0bj2vvfACbGtZgcnEFxo0dh7bGA1xzDB6YBIXybCaWFqspqKJdpdjDBi9otTf9QAAkBYP47FArmnztEJnP24IGmg8dRfuxAEKUoCnAFSIqBUImEAgZGDtuPH75fw9i87o1mFpSibHjxwEb/+c5f0KoBW1H9oMSgragCdWnQGvVQAnBHp2PjxYMLL3ph/Hjx+O6u/6M99auxtTiSiT5FLy/oQGTiyvw6x0UVu4yjJ86DV8/ZwnGjhsHvSUI7OhdqrJwHDrwmb02U6pgSl4+Whv3QyUEe/cG+iTAZVEg0HQMe8KkrlcBNLUCTei+L5oONuPeM3PxvVd22ccWl43A1OE6fvyf/slBjYcOIkXnbhBtQQMrPm3Fw2sP9vxFgD9TjKGR8hGyR23FkQPNCApLjWHaGYiChomQAYSYiaCVSq61XYWp8jiDtjZuqVAoEKIUwZZGNB1UsS/kR7t0sXB4+eWXUV5ejlGjRoV9f/HixVi8eDEAoLKyEtnZ2YPZvC7pqR1HlGa07G9Bik9Bu2Higm99H76kFPz++qvh9g47beooJGWMhBIyUTb7VODj8Inuu4LlFsFkBCeNGo70EaNwRG2FGjKRaKX38qkUfpUia3SGJ+VXd6jN7TiqHUPlmPSwgU2+tAB0hSLFKkLQVV+cfNpZuOPhZ3DVan7HueWnINGnwqcQAJ94PktVBdcteRInTStHkq4A+G/vOsBFwJeKxGQFibqCQMhEeoofmkIQNBgKspIHvBBAb8bmfwN+UMInqOzsdOdYWwgGY1AIQZKuwK9REBA+sQRNtB9tt7VmjAFVJ58GfeIR3PSfD5GTwDD38u9i65YtOKYmobCsCprCK/Gl+lWMHJGEYYPoYtGbftjR5oNfpQiZDNnZwwAAO4OHYZgMSlsIBjORkaBjohVUuKuxFQHDxDFfO3yWgKxQAhowYJiMpzBjfCPUFjKR4leRnqgjaJjwqRTZ2emDHpQ1mHPVXqPRHj/tVol7xfIHNBlD6diMQWtLOHrqi12hw6CEC8hiPACA/1gAB9CMQEsAiZqCUZnJGD+Ma+YyR5l493+H0RIwUX7K6SiaNRdtQRN+jcJgXEsGcE1xoqbYJusUn4oEjcKnKsge3VnLOpD0Zkz4jwVwiBz19ENr0MBesxFtzQFolCA9hQvI08dl4N3/HgZjwOHWAMpOPg0FM+eCMeuZgCMgk2euxcHKYvx793soqp6JMfllSEvUkepXoSoE2dmDN0Z66oc2XwvKTzkdhbPmIhgyMTrVjzlnfAHN7SGQj1ZD6P9PKixF2ckTEDQZTkpnuCpvL+7a1r82ka3LrWweBKd+ZSEqP3cGNIVY4ySjT5bYvtBVX+wKHUZeSgiAIyB/oWQ82kImgP4JyN9/ZSeWXlQOlRKwoIElL6/v9XcTMkbCZAyjslKQpKvIHp6IXaHDCIT4HHssaNjKNMPkYzZomAiYDMxkyEz1gwEImSZCx4JgAFRC4FMpgipFxshMjMxIjM3MMtG68OOPPz7k3CsAXlzD7YpAAMw7/xLkTJyK21btx14APzrlJMwYN8wKlCBQ+lHplBXyJOQ5VrCOY7ZwTCfuiky9QZgku4r67415SFBYXgWsXuWcF9YENGk4Xv/Q2bkSQpFXUoGg2X//wNagwYtCgD90bhNrLFTJAhxXio7HEjQFxwJ8I6FQgjHpiTjYEsCxYIibo6jLW5BwBzmydzu0v12F3Qd24RFmglAKXddx85+fRklFNTQl+q4lXeF2CRAI/1nhNk8INzOOSPYhI0HDut1HoLnMne4CEqTDoBfZWQhxgkGGMuW5aajf2QjAyjJjZUQB4sO9QoztcOOVEGL7lAutMCDyOls+uXCPCZERhtj+6TzlpdMvPlXByD7MY4NN+DmQOH6e8PpkCz9PiEeAdJ5nlD1b8eY/t8IMGXj6AQ3X3L8UObNnefy7YwlKHHcYlRJsWrsazz35OIJZXwYo/+1EikNCgNee+TseuOUa4Mp/9PlaN5ap+M19KxCy3HROPftcJ8g1UjfUR0QKNTc7G1uPW4AU6d0AywWiD46/BMDIZB9GWRs0t7+8k8QNVrU9y+0NDIwQqJayioAgxaeiuT1kuz4pCsGoZD/SYmSd7khUfJBbWlrw2muv4ZxzzonG5QcUlVJoCrXHnhhEReVVGJU73jrmDlQ6vqjV0anOQ8MrTR2f/2GkFtXOSeT4QvW92ePx5QLHaiDWPTEnnB/quphIlwhBknDNuX04hib/cL7dhIAXxHD5AybrCkan+qAr3M9LgeN7KITsbWvrYBzcDViR3aLa1Ob6d6ArxPZdTuil5WBwcYIWnSPOxAp4yyKLzB+Jlt+yLfxRMabglPEmvMCIOGt5rjdQaShCiBP46WwK+L/ZqdHNSHA8iPlAU2nXgU+uBdqd+s2dEaVjtPzY9ARkpUbf1zYcYq/X6Ri4oCjy0nec38Xv7f78E1+rQGkGn1xH5Y5DyCp1HAoGsa1hle2K5c51Hyu4N8Cb1q3BtxedjReXPozQ0SMAgPHJBOeWZIMQgu0b6vHALddYFTb7zqSCIlz7pydw0feuxvVLnuAZT6hL2RSFNSQz2ddpraAR8NUV1fUaW4OeDWdPiN/Ce8x5LYRdO5YE8Mg3BM4xEV8i/JIJCMZkhM81HQtEpVVJSUk4ePAg0tKG5gImBtPh1qA9kCghGJfBF6y0BCua3zXMHr+orF/XGt1BgwyIFCqkzw/U8EQdk0Z0X+K6t7jNUsJpn1ibAffDyawPiPeLq8LnhuyKRaXZSPFpHq25W8MSK9i7bRLumLehGYk6poxMAkC4O4ur/wiA/IqZvKqWC0VVUVzFc0gTEJTnpsekgBxOu2lr/7r4vQjhJjkRBAJYGweXUCS0Ye4o6aGuPRYUjU7F8CTd7tMUn4bJI5OQnRabgqAb0s2zSgmBz9IWd1zO+abKZSFwCcTuv6m1EIt5IRYD9HpCbJwTNAUqJZg6MgUAMGUkn6vdihH7eQAwPxdQ71mA3R9/4DoXRUFFjZ3pp2iQXU16A7FuggBYt2qFU2r66V8Bb/0FVQfeRmYyH++b19TBNHquINgV76xei60NdSiqnom8sir4VQVJutqv9TNSjM1IhL/DxkWhXW8Uewuzqul9/7nNvf7O4hljPWNKwDPOuDI0EfuVRygWxUDsORlegTvW5+jYFNvjHIVYpUOJyMbKR8Z5Jdm44YwpmDQiyasJI+j0QPSGRARsjamjOXHMjkDf0qdQSpAeAUf5kck6KCGYVzgK4zISPK4b4cx6jnaVIL+ksk/X+mJ+pv0gAt5sCLGUyUFogN1lukXrVOrswFmH77jTxIl+m1JSgTnzFnoEgYUXfA2F5dUAOX5Nw0BC7F/aIX9UMi8N7lrk3aZAYXJ0NlrEzgcrNMcdtRyx9NsPNJpCPZqZKSOTkNxDOfhYoWBUCtITtLCaOq5lCi8YEGs+UTosvh4tFmBnIxDaqlinq3sVWlVKiB0DkurXXP3jdTuiBNjaUAcjFPScy2SmR5iONWzXCUvQKp8x2yk13bgXdO0LSEkfZrnfEEyrqoHm03kauH7w/379Ezx2z2342cVfwWtPP2r3p+iaaAlwHS+r0DAH+8hFj63DzsbeBez+6rTJeGRRCT43kVdppZQgaHo3InYfgVgWQKuJrvGaqKuu5855MG1B+bjuaOCRAvIAQAhw8FjQLtIgJjhVocjLTAaFU2ZRJc7C1ldGp3TwSXKdq7/njAQjk31I8am4sDwXt3whz7PLDIdnYetHo4XGgZ+LIN0K8irLiR0Lha4oyMtMxuSRjoZe9IlPpc4E4/qO7Vtob3qc37f2ywug6z5QSkEVBSVlZUiwAvxiceETiLa5fcMTdRWawt0jEsKY1IXmWCSit/3kiTMxA+B5cxHdsR8tfCq1N1nxRKKuYvywRFsb6sbe8BMCFmanr1sBqQp1a1Bdy7FrsyhcT8KdJ5boco50WZA8x+FsEMT3CQgI5fnTFdWr8GCMYUtDXUw/H7aSgxAUV1TjJzfehnGTpoBSCsYYHv39DfjPM4/iiQfuAgDc9tDT+NqVP+/XtYwQj/8wDQNLfvMLbF63xh5H0XyWOrk0sMi056cvbu3xM9+bNR4FWcl8bbGeJ5UAbcEOArJrzA1LdCxYmvU9APBrXMR0UjY6Ffjc54hVpIA8AFBCkKgrSLN2+m7tho11LMmn2JNaX5mcPcJzPuELpCoEutJZIzmY2IK6/RARV2Us53PclQCWcN934e6DjQ22ZkUsIrlpfqQnaAMWfdwfirNT7dymgpy0BFufqlCCfFf+U8BttnL6cvuGejz/0H0glOAbP/s1CKUwTRPX/uwn+GBjvf29WIUQHuwZrpojJbA0w8TOxgJwcx4BL2iQoFErp6ajORMLqt/Kz3kiMjLZh9KctLgMTORZVzpbrtyCX2cXC745ooRnf+Ea886uBgp1nqEYmg7Ckqgr0JTOSzIlXbedgI97zdopivskAKaWVOJXS55EVe1ZoIoCYuUOLqqeGdOaOyE8qQR4r2E1/nDjL/G/HR/ANE0wZiIQaMcDt1yDR+66Hdd/8zxQwnNi9wtXhT7TNLH+3ZUAoq9h7zgKnOqg4bmsekzErj1n0nCuxKMuK2eYgH/H751gwvAke53ya9RVOKTDPRDuh+zXqC0DxTLxYYeLM4g1CMTP756w3ZH4AN9ttcHo1+Q9ukOwCQEfnCrleWYVQqFFaVWw/UHh0urBSSgOAKdOGo5LKsc4k7r1QH1rxjj8adV/e7wGefIX2Hr2F1FcUe3ahMT6I+cwOtWP/UfbETQMvtB3EKBt07GlMd26th7XWaV1n31Qw5wvn8vLsJomAoEANq2pw+TiypgWBLr2M+YjhBKemaGTBkVo0BgfPT4FOGadj4FrGHXVed5OxJ2/Qrnv+VAgPUHDpBHJ2Pxpc5duB/a/jBchEBpjUQqXWoFtJmNRF3h6g09VUJLd2erlrB+dJQpCuBuWqMIpLJOiWNTk4gpcfeefseO9BmxpWIXCihoUlFVa/TYYd9V3xFqpqxRr312JYDDg1fwzBtMwwBhDKMgr1homA3noQbDLH+z1dc7IYvhP4y67uqumaSifMdtes7JSoufD794n3XjGFL4B7Gb8fm7CMAxP0vHbN3ZErA3Ucl8DET784RsgDtvZY4SizrXWu5/XBE2BRnmqz1h/JqWAPADwCYtruHSVwqdSHDga4P7GhqhHzj/LhWmKkGliWKKGQ8eC3ZzZS44rCIdYOz6NUrvEbFkUo/jd2h/AEVwTNMU+PjxRs4VCkXmBEILTpozAsCQVty7r5mEPtUPbvwP5FTWWHzexi0bE6LwfFrHr7ijQORsq7p7hUyneW/OOU1o3yL+sahpCIUDXdVTPPJmbw/rpjzcYEHSdXcPZUnbxnj0RAyacbA0mY2Agtr82EPvCkKR7COHxEGLTq4T5QYWfqgkAzOWDa72vEG5JawvxFFNTRiZ7YgDiASGY9GRdE3OIqgihxkoJac0jBWVVKKqohmEynv4TBITE5kwp3GF0laJixixomo6A2eYIyYSAKgoYY9A0DaXTZyJkMKh//B16v3oCX5tThtkPPIN3Xn4alBCcfe75KCqvsi5BkJsevSww1DWHl+ak8SJghKB6TDpWW2kdvZ/nGVoigXiuGJw13K/STnOzZ5MKbiXd/tlRtIdMe91yz9lc9LG0yAqftyOVFGCgiN2VNI7hD5cfBASpPhWjkn3c11RxB1s5GjNd4X/fN7/I9lvuDe4UbwBPhO+kyIngDfUDhVBb4CGE2DtgAuBzE4fDp1DMHD/M1vzqCt9IiO8UjU7FoxeU4bEusntQRcO1f3oCk4srrFRfjo9qPOHk3OygMYXw0yJI1Cl0haJ0uqu0rqbh1C+fhxsffBKLvns1XvjXKyirmg5CnBLOsUjFmHQ7l2ZHPGVKw7wHONo0hQLJPifa3NayWZq2JD2+BCFJeCghmDgiyS4SInCGiDe1pR3HQIRvsjMfpvq1uHQ/Kc9N7ySMCBzfZGK5WvB88MKczbXOjhUPcPxCY7UnNEpBCIFGKUoqq3H/48/hKxd83VV22ocrfnkrLrny57jr0WcxrYwLtYwB+PsPgWB79xdwkVdaie9d/1t8//o7LEukmI+ji7vc9LGAYftEX3PaJORlJoX9TqTWvpBpOoGu1vjSlM6ZZCjx/qspFPmjUjAqxQdKiB0XkaApGJHkc53TCj4nThrPWEWuIgMAAZA/KgXv/vewfUwMlLagyXfAngne2VVdOfsk3LLsw15dJ9vlYqFbg1GYzqIdsW0XfgAsrSaBYRIwwpCTloBHLyxD0DSdSVvlQmArERM613B0dR9EUZBXWo62oMG1TJTYD12sB+J0hBd48KKrPKBz494mmIzf17SyKtz0wFPYsHolSqpnWaZSgikllZg+eST2NLUh+lN7/xEuFF2+B2dcizHFGIMJYq2O/D0xUUviH0IAjTq5nu3jrn8ZCChhUCj1WK5EdS/bPSGOsef2Lt8HEnSeQjPIRFAUswUSeyMJZmvjw/k7xwKUAj4RgEkoiiuqMa28CnPnnYu333oL6RkZaD5yGOXTZ2FaeTVChomNa96BaRjAvh3Any4GTrkUKD6r2+t8sLEBxRXVthDodiGI9j6KEoKrTj4JxwIhhEwTOqG2dSRkhFvfCJQIWQ7bQyYSfSqo6So6FOb50RVewMdtEXQHzYqvJOkK0hM0HGhphzBudHXOWEMKyAOAW5vhTdtFbLOXSD/k8dUlBMWjU/HzORNxWw++RARAlqWJC5kMIcME1b2J5KNJW8h0RZDD/tdkTp5E6jIbCoHaHT0shKKf1k7EHcu9/XHm1BHOIkmIrZ1P82sxmfy+J8L9Zkk+1fKHE245BKVV0zGppMKqLMeFgIDhaE/jTYMucH7LLt53aQntRR8EPpWgLWhapvjYGPuSgcejUWVcSE71qWgNhmxh0lm4YzuzS2+hJHzaTvvZt0x0IQJQwjzPi1hnVGsTkZns62SBjBXE5pcSYGxGAtpDJvY0tWJaWRUOtQRx87cXIhQI4gldw91/fw55JZUoruLWtWC7CRZoBd5+uFsBmTzwDdwWOIrrH3gS5ZXT4de8QWjRjmVpbg9hzsThaAkaME1mW0kJ6dpvt6sKuL1FIcD/zSsEwF0LTMoDA02En1eLRqd0Y5Hxrv2JuoJETUWTEbJde+LhmYzNLWScEy6Fia0BIyKQwsnN6Q5QA4DCrBRcM3dSt9e4oDzH1gC0BQ27rLQzKUZ39AUNEz5Vsc2dHhO4fUwE8TmR97YZxjoPIQSVY9I95760KhcXleda78PWvgMEk0cmH/dEMdi4f7eu3uNjh0CjwPsb6vH0A3dj24YGqArxVg+Mg115V9gm4TAI7bGzEbD8FBUKlVoVCeP67iXh6Oq5GJOeCFVxUkbZcyj1ziXi33hYjLtDPBvh8tQLlz3rH0sgdqXSgpNmy2+lglQosUp2xx7EWgDE08yraPLfdUtDnR2HEQwGsW7VShACFJZX49cPPInTvnoRVE0HetCmsuYDCIWC2FJfZ48jsZGIBTTFcRECOipAvI08rySbK0uOc5CPSvHZ5ay5Akso72BZZr2f785dya2w4K4UFAVZKR5rTrQ3Ib1BapAHkDHpiUjxKTxaE7AnL75Dhh15bGtO4QyacNHMbs4vzbb/DhqmlQNWTIjR15hMHJGEI7saeXuII+BQwqASIMSc9EUmc2sF3ebT8AJP0ApYEDOpW2Mfj3TXbrfbASXApvUNuOGb5yEYCEL9k4Y/PPIPTC2p7FEDGw90t1FwLxTC8kDAfZEVSj2V9VhchWlKuqOrZ2NUig9Bw8TuI22WFtlZdAmYnc0BcOaRuro6LF++HLW1taip6VvFzmhDCcFJwxMxLFH3HM9I0PDZUROUMJhwaRnhtk5a6wv1ur7FItlpfqiUoDXYarczPUFDcXYqVv33MAora6DqGkJBnnWiYsYsW6mSX1qFSUUVmPmFr+IfS+7Eey2NQFJ654vs2cq16aqGoqoaXmwGPCd3ml/FwWMBGFF208tNS8C+5oB3Leyw3gFAeU4aFhSPRoiZx/27hkzmuPLYGy1nfe4tzlrOA2Pd8SBCuREvSAF5ABC/vwhGamkPudIPOZOWM9hdu397Uu89BgP8QoOM7gWNwYISR2vhVLkjMBhPZ2SEDO4r7W6z+Nel+RDH50/LwrObPgUAJPucYBuuKBDid/w8eILRqX60Bo0uK5+5f09KCDasXomgK5PFxtXvYFpZldfkHIek+VU0t4dgsvBlY0tz0rD502Yww4DBXNoJl7AsXo9I0sOeQxJ/KIRCV7vWBgrNlvsZUagwNzubqk1r1+C7F3wFgUAAuq5j2bJlcSUkF2aleCxFgjEZCUjUFXywv8USaBh0hSJgmHDbYxRqaQQ9R2OPBE3ByGQdOxtbrd/WEVQp4UF1v37gSby/7l1U1cxGcWU1QoaTxo9QgiklFTj3Wz/E1sXnIvT9p70XeH8FyKv3oOJzZ+JLl1yB/NIq2+pQmMXjFgyTYX9L7wP9Bgqxhoq/NQqEzI6ygeUnzLyBff3BsH2O3Yo2a8NJ+hYAL9qdqCkei66Yp/lnjq+9g0Fs2ljinI6/e5JPRXF2qq0dpYSbAoUG1T5GnKmru8Hz+y8XeF4zWBoCQvolYA8UXvcA52+7TLBrERNtFz5y7lrthBIsKsvBXxeW4Ns1Y3HyhAzHpAqvz3K8MTxJR256Qpclvj1aMAKUz5gFzcpkIfJ2iglIjKN4ZESyD/mjkrsctwoldglzQniJYnfAnth0jh+WiJy0wU/PVFdXh1tvvRX19fWDfu2hTFluWpfBZMLkTF1zi0q5n22yrnisKrs2r0EgEIBhGAgEAli+fPlg3UJE8GtK9yZtW5jiWW/c5m0QIEFV4C6mFMuIcvLuZjoiG9cUX/ydH6CootpeW3SrRLQYKVNLKzFn3sLO5245DATbsbFuOYDOmRgALqTHwibCs54TAl2lnVJ4mnzxBwGBX1Pw/dkn9ft6JnMrpxz5BAAmDE/sc9q7cOPMrbyL9XEISAE54miK4zfkPU4d04W9K+Rlkd0mDdgmDoK/LirtdJ7xGQme/MfWVzpFm8aCGcNug1vT594UwPmbf4w4+ZDFMdcGQqEEsycMB+2QQk7ojmPgliNOdmqCR0ieVl6NPzzyD3zt+z/D7X95BrNm1tgLSopPhT8OAxQFbr/JcIxO9WP8sERQws2h7ok2mr9/XV0d5s6di2uvvRYLFy5EXV1ddBpygmH7nXf4/QnhbjduH9zTTj0Vuq5DURTouo7a2tpoNXtA8M6tbt9VPjsq1BE642Ge5HO+k71EBG8TV3Cy+IxIKWanvLPGQ+2XF3Q6Ly8qZCIUCmJrQ53dP+55JzPFh4oOcS/RwG1xFoXHtA4Sm6fYBiGYM2l4v6/HLG203YdW34DwDVpfYnvEb9ARTaG2rBLNQiy9RbpYRJhhiTpSu0xGz1ORCZMIAEwakcSLiEDsnJ1CItOyOqeqEtpizzHm+JwBfHAWZ6dG5H6OB+EDJ9wHKOEFHmyh2JrMddWJIFYogV+ldu5HIUgLw7u9GLq0pgkxntfzeMhO82P3kVYAQmMOTCuvxpSSSvhUitGpfjS1HQUAZKX6kZUa+5NOV2gKRUkP49ZTDMT1LyUE4zMSu9TEDyTLly+3tZPidTyZ7+OVkck+DEvUse2zo2gNhqzxwBd5S7EGgFulqmpqsGzZsrj1Qe4OIRgLH2TA5d7mEng6WiljFSH4Fmen2tYDLh4S218Y1jNfmJWCjXubrHVCKFf4J/LKqoANjkXHRxnMLa/BUBSoqoaCipleTXsMYRfpINx3XKQs7FhKvj1k2K4zbg16fy8q+t49doC+r632eTp0bEl2GnYcaEGKT0Vmig97jh0Of4IYQQrIA0BX0cGUcCFAVYj9GnBMREGDIWAAztLfmcumj/G8J0pKux9ycZ3o42QaAIBkXcWRtpAVYOdolX2KV7i3H0w7eI+AWLcjyl8KDTK1dreixOVQJNmnWhsG8FLLxMk9OTxJjwlrQaToKbI+LUFD0WguRHs0yIS7aUSD2tpa6LqOQCAATdOGnHYyllEoF5L2HGnD3iYrYA+OoiHVr2LSCF5YoaamZkgJxoIkXUGCpuJYIGRLMiJ4lcKEX1VcChj+rMR6ICsJs4bxtUKxA+iINfcLrbitPLFev7+hHrN3v4QVOV8EAPy/c0uxs/BObFu3CgUVMzCpqMJjZYg1nHsivEIi+G84MlnHts/4Z5grgI7Y/9c/poxM9vSfUDzofZQlhLtnWU4aaBit88QR4QudxCJSQB5ExEBPUBUETdMj2PhUirYQ15N2taO975xpGJ6oe94LGCYSdS4gTxyehP0tgS4SiQ8+YkdLCRfyThqWiHW7j9jaADs3pycDh7ODFVogSgDKOp+XAfZmg4B0ivAeKuRlJmNPUxv2WBH7lHBNqulaKE4kOgZt9TWAJNLUuLSThYWFQ1IIi3Wy0/zY39IOM2RpkJnXf3Mo41MVFGaloGFnoz0nUMLN8aaqeCxshAAjknwxP1d2/MnEmujXKI4FuaVGPPO6Sq1APbEBIHh/Qz2u/+a5CAUCwOxWoHoBPt68juePd51Xod7sUbGEW3hP0HhRDkKAb1aPRXqCin9u/gwJuuObTkj/7+PSqjGonTgcbhdQcSpesbT35xqZpCNJV8IKx/GGFJAHEeE7pVACgxGPJsyzAyaODvm60ybj1//5AABPHj4iyeeZ8IMhhmFJGgKGifQEDRkxNPHZ7hOuKcn2bSJen7mS7FS8v78FrUHD7gQRPevpH2EiJABhQKLGH17GgAnD42dn2hecQjKOeVFo2Pn78Vc9MBK4599or29CO7lnz57oNuQExq8qXFACAMJiUis4kBRmpWDrvqPcmgbC/T2pIzgKxUPHst2xSEfZyu0/7dZyAtxs//7+o2hsDSJovbe5YRWMYJB/YMUjwMq/YeU5F+Gtfz6BYDAITdNw3ZKnMPZzs2GYLCZ8jjviyAOumBwACbqCRaXZSNZVnDJhuMdfuL9jfuLwREsbz1OxAo7/d1/lXEpJl1mZ4o1YsMOfULgFHb1DgQfF9rnln/nk0DEUZ6diybnFuKx6DHJSfR6/IAAImiaSdTWsv0+0IYSneXELcO6Ic3ewIiEEk0cmISfNb2sEies9T8YCVwJ1XSGeyXKokpGo8c0RvK45QGyaBwcDt0vRlJHJ0W2MJOpMHpGE3PQERxg8wR4Mv6agLDcNKX7VFprsQC84QnKswwPwOrdTBOSJdbMwy4lXEPOiCOYrqqqBojnxCKqq4cjB/QgGAgBjCAYCeOvFp+1aBLFIJz9y4V5ICCihOLswCxkJmuN/7NL+9pXdR9pcgf78egl2dcHjEb3jm6Eh5scJHQdgp+P2gs8f8raQCb+mIElXMGfScPvhcA9VgzEk+VRMiEG/HjEhe6oBoWtNgKZQHo0MK+iE8OhdxmDvat3fE+YgtzZ1qJKgKRg/LJHXs++gQY7FzdFgkOrX0Gz5tKf6Bz84TxJbUEpsgYcxXqgpPeHEXOLCza+E8HzisY6q0LAa3bKcNLzzySEk6ypMxlylxDm6SmEwoJWYyC+pxK1/eQb/ee5JHDywH6kZI9B0+ECnc8bytEndv6HldEhcx/lnxNrKLQb9LXDC4LK4EGIHPKpRdl+LNifm7BEl3K4Cbr1foqaiNWjYwWsiWbdCnRKqjDklVT1qf0bCJpCPBdy7Xwan3ORaq8KeOwm6+xnkmmcVzYEQd0cxXYngAacEpigYYXssD31GJvnwaXO7a9IECrJSOi0WJwJTRibDMBlCZvjiIpITEzG9pvpV+OI47eHxIDJWMMtNTRzrS6quWIP2oO1VCIVGrWIXhOdLNkyG6y4/D6FgEFRRoKgqTMOAomk4dd65Ql0zWLfQJ2yrqZ3qjrc0UVMQCJkeV0WNEmgKgRli+PO5xbjsqY19vxoVVgYg2adY1/TWVzjRkALyIKIpFG1Bvpi756n8UTwI65NDPJ2XmAjcj65bOPRKk9zNINagrgcrXBJ2W+Npbxisl4TftU+lOBrgeaVNxj9ILHWIO9l9x3+HOuOGJWLf0XZPIOeJKBwLFEqg0BP3/iVeRB71WM/SMJCk+FQ0t4VACANcwYrjM2Lf97hnrLWRdZ7wraXD3hxQAmyur0MoyCuPggCnnXMBsnLGIL9iBgrLqgDEZnElbjW1FGauWBMKLggDbpcKXhxGsdbc9CQdKiUImb1/BkYma7ZYoSuU582Gkw3mRCU2VY9DlJLsNJebgfNUOsnKYedJJoA1SF1+y7b/EbGnfwLEpJYk2aeiMItXO0vSVWS6UnARAvgUaid7Lxqd6ol4dbtg6K7E4hTOjln8LXYLsTjJDRRu7bFEInFIS9BQnJ1qZXE4MZ+R0al+XpVSKFUI90+OVhrESNKdvlesrapVJIYSguKqmVA1XnlU1TSceva5WLT4SkwtqbRzC8fqXEqpuB/RTuu4y9VSDHFNIfYaSsCVbr3l3vmFKBiVap/f7wqsF77NJypSgzzoOOYSz1Fr15ugKQiZjO8gXdWPAMfdQnw1ZDJolMSs2cxvBeiNSU/wRLUSy31EoTxVWUcBn2sIeOo7TbGSw1sbBLv6HhHaIp7qLTM5fgtk9JV4CLSRSKKFplCU56ZHuxlRR2hSc9MSMDwpdrIbHQ/dWQzFuuBXKUAAwwTyyypx44NPYm3dCpTXzEZeaaUd/CbKNsfqdCpcYtxFQoQfMtcNEcvd0IntEff2o1Mm4PUPD+BvDbt7vE6aX/MopVTF5VLBHJ/+ExGpQR5kupNliSXs+a3KcmLwA0Kr6gjJABAyzJhPp0IJ6fRwEdeDH07YEztkkSJOVSh8liaZEFiFUazyoiAYnqhjdBxXkOsrhVkptn+6RCKRdMSvKshK8XNrnUpjpHDU8dOVP+zYjATkpiWAEh6s57eCvQkhmFpSiXnf+B7ySyo9uYW1DgW7YonMZB3pCdxVwq9SZCb7MC4jwWVJFpX2+OfdFQEJ4W53Z0wZibu/UtjjtZzgf34yxdXBlADjMhLtvjrRGBpPTRzhNl10fs/ynbIeio4FEMTDIf4OGCaSfLHnXtETQlveXdlTvsN3+orvavlD7FMJFMrdL2J19z+QrKtfjb/c+we8t3Z1tJsikUhiEEoJctMTot2MASHcquFTFaT6VQhLKw9yd7tkENs/mVhrrBD6YnENURWK7FQffCq1Nbqpfg1JuuqJT7LT+Fm+yIrQJls3NbIHt5qvV+Z6XHHcMgbAzzUy2Vt74UQittWPQ5CuTERCmypMHZqqQKEhwDXYKbjvrQEhIDOk6LH9E3a1EbD/DvPgCQHa2ck6/lZcWKYwTdalNmEoU1dXh7lz5yIQCEDXdeQvWyYrt0kkkrAQMrTy+/QUlO04IXAXiqAVZCb8eYXLYqKmgBKCJF3B2IzY3Eg4SiR+P7pKkT8qBSs+OujJOAE4a6quKqABo9Pxrjhtygj7PMRy4XAyZ8Xm5mEwkRrkQUYMuOQOgu2IJN0KrOA7QUfL6uwW3SaUYwEDJhj8MZ7BIJwQa++ASfgH2H2v7te6pUVWhEnI+v6J5B+1fPlyBAIBGIaBQCCA5cuXR7tJEokkRhlq8o24n67cImxhD9y1xA5mE2uqdQZN4e55mkKRGKNKJvd67z3uDt4XAf78/jSFeNZVQggev6i8++uAX8QRqkmnNfhERQrIgwwlQKKuIG9Uiuc4IYS7DMAZ3IoVjSsGqrsgRIpPBaxAtlimMCulk5/0yCRfD8EWwihG7NeUcDOaSGXj9MeANj/mqK2tha7rUBQFuq6jtrY22k2SSCQxSndubPGImO9TfOELAwmNqxCUheu12zXR0ZgORov7j8hs1TFOx3GvcAR+YrlWiPVR3FzHGgNufvK5CYBbGIajeHJf60QmNrdOQ5js1AQk6uG1vo5WlD8YGgWCptsfyBnwlBAYjMW8gBxOwz1uWCL2NrXb9+Emza9i0ogkvL//KMStc8uPU1XPPcGdaNWyampqsGzZMixfvhy1tbXSvUIikXTJ1MxkJHWx3sQjo5L9GJ6kdav1FesF1xjD8T2G12c35gVkAggR1W0l9SjNKLEsq877XLHG6y2EW2MBoGR0KirHpNuV90RCAL8V12N5W5zwGZNOHMkiRshO6zrbgnswE8J9bSlhfPfL+L8aJWCM2g+8HqfRyV36kBGCtATN1hDbx63vJOrUPkIJj/Y90QIIampqpGAskUh6JNazHPWVMT34CwvfWcDSqFJqa5QJAXSVWIKf8FeOXWyFWBgXRZVS2+1Q1EvwZLQQn6UENEzBkKBp2ko24bKhUp5DWrhvgDCUxUFp8oEkPqWrIYrb7UBoke2drvARItytglLCAw1iMUdNL3EHGXTE7aPtVEYinmMZCSeecCyRSCSS8HQUKikBknyKvZZqlFopMuNBg+wUEOtIokYhynmI9VF8zI5bEoIyIfjtF/M93x+bnmBvGoRGXfSb28UxnuWLSCAF5BjCHqTiNRwzkXvgUutYPKZ4E3TngwwAeaNSnKAB4hamRR8QTByRNODtlEgkEknsY7soEu/6ohLum6vZhUGI7Z6X241FN9q4fYPdbpm2QEycTxaNTrXlA08eY+vzE0Yk4aRhXPt+SWUuLijPdgX6OYoqSoCynDQUjU7FtNHeOKkTESkgxxhuQViYTnxW0nNRGEPkcYxn85l4OMdlJHb9GeuhTfWLgAwnoEDCU77deuutqKuri3ZTJBKJJKr4VYqx6YkYk57oUTKB8MB4keFBsbSt6QlqTGeBEoJ+WU6apxIisS3LxPav1lWKotGpyEjUuNbX9RkhJJ9TNBoAMOukYdAVBcRVmto+nyulXMcKtyci8SthDVESNRVTRiZh22dHYQYNEMLLT7eHTPhVBQHDtHd7yXEcfCEe/u4SmRdmpUKlBIbJcKQ16DE1neCWn075kJfJfMgSieQEhhCCzBS+nuxqbLWOcSFZo1zoE8d0ldqlpmMVYVHuyKhkPz45fMy7CQC/J41S2x1RaJkZAMqAqjHp+Nv5pVAUl2DML+QUCYFUQLmJ7RFyAlKQlQJVoRiV4rNKLTtaZbEDpoSnhPPH8Q5vXEYixvZQ6SlBU+wSqV7fKPkQy3zIEolEEp7ibK5NtX14bT9d/rokOw1KHGhZMhL0Tn7AYzISeIC+6qS0c0MAx88a1rrZKb7J8VnmwX4U8eCXPdhIATlGGZnswxjLkZ4SYqdzE8+KL85NICOTfRjRQxlMgdgMiIebgGDKyOQBbmFsU1sr8yFLJBJJODSFa1Od+BV3gHf8SIFdxdkQQpw1saOATIAElfKy06RjqjcnsM/20wZsZVw89c1gIF0sYhgxYAmYy0mf74DHZSQOqfyW3UEpwbTRqajfeRiMAKpCXH7JJyYyH7JEIpF0D7ECuwmYLRBWjEmPYosiA9cSAzA6uxtSK0aJWKpzLiRb6dxE2lgitM/EThcbstLBycxQDlERkBsbG3H55Zdj06ZNIITgoYcekgt8N7gsJLbQnJZw4gmImkJhmKY0A1nIfMgSiUTSNUJbalqa01jOe9wXHGsqMCKpsyWWV9VztMSMOQHu1PWf8EMWx+Xa6iUqLhZXXXUVzjrrLGzbtg0bNmxAfn5+z186QaH2DtjyvyVDYwfcH0qy0zyJ4CUSiUQiCYeqENvveKjFrbgLio0f5s0EJdwu/KoCCsL/c7tUWJktbPcM29ViaJUljwSDLiAfOXIEb731Fi677DIAgK7rSE9PH+xmxAVO6Wl34vMTewif2HcvkUgkkt6QleJDQVZKp7zIQ4mOIq3jc2ylhSWOtll83hGKRQU9bzIAicOgu1h8/PHHGDlyJC699FJs2LABFRUVuOuuu5CU5HVGX7JkCZYsWQIA+PTTT7Fnz57Bbmon9u/fP6jXaw+ZOHKwBSHT8TkyTGCP1jqo7QjHYPeFoOlgMxRKsIe0ROX6HYlWP8Qash84sh8cZF9wZD9wotEPjDE0HTwKxvj6aTJgjxr/62fTwWZQAoRMYI+vzT5+uCWApqPtCBgMhmHCYAwG41bXtmAIhgmYojw1IQgaJnx+FU3tChgDxg1LwJ49x4739npNrD8bgy4gh0IhrF27Fvfccw+mT5+Oq666Crfddhtuuukmz+cWL16MxYsXAwAqKyuRnZ092E0Ny2C346SxwLv/PQyA10o3GEN2dsagtqErovGbqCntUCjxJE6PNrEyNqON7AeO7AcH2Rcc2Q+cwe4Hxhj2GI0wGV8/zSGyfu4xDoOAIGR672c0Y2gNGli/uwlB0wQABA0GkwFoDyFkmPCrFKrCBeRAyERGsm4XIZscBffNWH42Bt3FIjc3F7m5uZg+fToAYMGCBVi7du1gNyPusMsuR7cZUSczxRdTwrFEIpFIYhd3ft9YLw7SW9w+yG6IVSNB+F5r1Er3Bm8FPpG9QrEyXfhVRZaWDsOgj5asrCyMGTMG27dvBwAsW7YMBQUFg92MuMLtQyV9hCQSiUQi6RlCCCYMTwQlwIgkfcgIgWU5aUjxq2Er7YnYJUq4AOzXFOgKsTNWUAJQS9WWqCl2qel4rqswUEQlzds999yDCy+8EIFAABMmTMBf/vKXaDQjbhB5j1P9qowzlUgkEomkl2Qk6qCHjiEtQbMrs8Y7hBBkpfjC1kJwV8gjBCCMa4wVQmAQ5ijcGIGuUql064aoCMilpaWor6+PxqXjEgKCVL8qd3gSiUQikfSR8tz0aDch4qT6tS4LZjnpYR1BWVMIgoaV5o3weCbxPpO5U8MiK+nFASdq3mOJRCKRSCS9x0ntJjTFsKvlHSOmnRHLZI47hiQ8UkCWSCQSiUQiGQIQu4w0s+sHEsKzeIhcxyqlUAizfZJleenwDA2HHIlEIpFIJBIJKsak2/FKwt0CBHA7aSqUu1rkpiVg8oikcKc54ZECskQikUgkEskQwl09kFrV9PyaYhUJcZWk1hToqhQFwyF7RSKRSCQSiWQIQQiQkaghM9kHAgJKAL9GoSlO2WlJ90gBWSKRSCQSiWQIQQD4VIoETXEF7hH+GtwXOVweZYmDFJAlEolEIpFIhhC2i4VzhBcPIV4XC5nirWukgCyRSCQSiUQyhEjQVKT4eKIy2w/Zlpa5kDwyyWd/RtIZKSBLJBKJRCKRDCEKs1LsQiIiSE/ok0V6t3HDEqEOkeqCA4HsGYlEIpFIJJIhiK5SKy8ybJ9joT2WdI8UkCUSiUQikUiGIKl+DWU5aQDgyo1MMG5YYjSbFRdIAVkikUgkEolkiEIp6eCDLOkN0jtbIpFIJBKJZAjD07xxZHq33iE1yBKJRCKRSCRDGOIqDSI1yb1DCsgSiUQikUgkQ5iKMemglmQ8Qgbo9QrpYiGRSCQSiUQyxJk2OgUqpVCkj0WvkAKyRCKRSCQSyRDHpyrRbkJcIV0sJJI4oq6uDrfeeivq6uqi3RSJRCKRSIYsUoMskcQJdXV1mDt3LgKBAHRdx7Jly1BTUxPtZkkkEolEMuSQGmSJJE5Yvnw5AoEADMNAIBDA8uXLo90kiUQikUiGJFJAlkjihNraWui6DkVRoOs6amtro90kiUQikUiGJNLFQiKJE2pqarBs2TIsX74ctbW10r1CIpFIJJIBQgrIEkkcUVNTIwVjiUQikUgGGOliIZHEETKLhUQikUgkA4/UIEskcYLMYiGRSCQSyeAgNcgSSZwgs1hIJBKJRDI4SAFZIokTRBYLSikIIRg+fHi0mySRSCQSyZBECsgSSZxQU1ODO++8E4qiwDRN/OAHP5C+yBKJRCKRDABSQJZI4oiDBw/CNE2YpindLCQSiUQiGSCkgCyRxBGyWIhEIpFIJAOPzGIhkcQRsliIRCKRSCQDjxSQJZI4QxYLkUgkEolkYJEuFhKJRCKRSCQSiQspIEskEolEIpFIJC6kgCyRSCQSiUQikbiQArJEIpFIJBKJROIiKkF648ePR0pKChRFgaqqqK+vj0YzJBKJRCKRSCSSTkQti8Ubb7yBESNGROvyEolEIpFIJBJJWKSLhUQikUgkEolE4iIqAjIhBGeccQYqKiqwZMmSaDRBIpFIJBKJRCIJS1RcLFasWIGcnBx89tlnOP3005GXl4dTTjnF85klS5bYwvOnn36KPXv2RKOpHvbv3x/tJsQMsi84sh84sh84sh8cZF9wZD9wZD84yL7gxHo/REVAzsnJAQBkZmZi/vz5WL16dScBefHixVi8eDEAoLKyEtnZ2YPeznDESjtiAdkXHNkPHNkPHNkPDrIvOLIfOLIfHGRfcGK5HwbdxaKlpQXNzc3236+++iqmTZs22M2QSCQSiUQikUjCMuga5H379mH+/PkAgFAohAsuuABnnXXWYDdDIpFIJBKJRCIJy6ALyBMmTMCGDRsG+7ISiUQikUgkEkmvIIwxFu1G9MSIESMwfvz4aDcD+/fvx8iRI6PdjJhA9gVH9gNH9gNH9oOD7AuO7AeO7AcH2RecWOmHTz75BAcOHOh0PC4E5FihsrJSVv2zkH3Bkf3Akf3Akf3gIPuCI/uBI/vBQfYFJ9b7QRYKkUgkEolEIpFIXEgBWSKRSCQSiUQicSEF5D4g8jJLZF8IZD9wZD9wZD84yL7gyH7gyH5wkH3BifV+kD7IEolEIpFIJBKJC6lBlkgkEolEIpFIXEgBWSKRSCQSiUQicXFCC8g7d+7EnDlzUFBQgMLCQtx1110AgEOHDuH000/H5MmTcfrpp+Pw4cMAAMYYrrzySkyaNAnFxcVYu3atfa6rr74ahYWFyM/Px5VXXol481yJZF/87Gc/w7Rp0zBt2jQ88cQTUbmf/tLXfti2bRtqamrg8/nwu9/9znOuf//735g6dSomTZqE2267bdDv5XiIZD984xvfQGZmZlyWlI9UP3R1nngiUn3R1taG6upqlJSUoLCwENdff31U7qe/RPLZAADDMFBWVoYvfelLg3ofx0sk+2H8+PEoKipCaWkpKisrB/1ejpdI9kVjYyMWLFiAvLw85Ofno66ubtDvp79Eqh+2b9+O0tJS+7/U1FTceeedg39D7ARmz549rKGhgTHGWFNTE5s8eTLbvHkz++lPf8puvfVWxhhjt956K7v66qsZY4y99NJL7KyzzmKmabK6ujpWXV3NGGNs5cqVbObMmSwUCrFQKMRmzJjB3njjjajcU3+JVF+8+OKL7LTTTmPBYJAdPXqUVVZWsiNHjkTnpvpBX/th3759bPXq1eyaa65hd9xxh32eUCjEJkyYwHbs2MHa29tZcXEx27x58+DfUD+JVD8wxtibb77JGhoaWGFh4eDeRASIVD90dZ54IlJ9YZoma25uZowxFggEWHV1Naurqxvku+k/kXw2GGPs97//PTv//PPZF7/4xcG7iQgQyX4YN24c279//+DeQASJZF9cfPHF7IEHHmCMMdbe3s4OHz48eDdynET62WCMr6WjRo1in3zyyeDchIsTWoM8evRolJeXAwBSUlKQn5+P3bt34/nnn8cll1wCALjkkkvw3HPPAQCef/55XHzxxSCEYMaMGWhsbMTevXtBCEFbWxsCgQDa29sRDAYxatSoaN1Wv4hUX2zZsgWnnHIKVFVFUlISiouL8e9//ztat9Vn+toPmZmZqKqqgqZpnvOsXr0akyZNwoQJE6DrOhYtWoTnn39+UO/leIhUPwDAKaecgmHDhg1a2yNJpPqhq/PEE5HqC0IIkpOTAQDBYBDBYBCEkMG7keMkks/Grl278NJLL+Hyyy8ftPZHikj2Q7wTqb44cuQI3nrrLVx22WUAAF3XkZ6ePmj3cbwMxJhYtmwZJk6ciHHjxg14+ztyQgvIbj755BOsW7cO06dPx759+zB69GgAQFZWFvbt2wcA2L17N8aMGWN/Jzc3F7t370ZNTQ3mzJmD0aNHY/To0TjzzDORn58flfuIBMfTFyUlJfj3v/+NY8eO4cCBA3jjjTewc+fOqNzH8dKbfuiKrvonHjmefhhKRKof3OeJV463LwzDQGlpKTIzM3H66afHbV8cbz/84Ac/wG9/+1tQGt9L8fH2AyEEZ5xxBioqKrBkyZKBbu6Acjx98fHHH2PkyJG49NJLUVZWhssvvxwtLS2D0eyIE6n5cunSpTj//PMHqpndEt9PZYQ4evQovvrVr+LOO+9Eamqq5z1CSI/ajQ8//BBbt27Frl27sHv3brz++ut4++23B7LJA8bx9sUZZ5yBL3zhC5g5cybOP/981NTUQFGUgWzygHC8/TBUkP3AiVQ/dHeeeCESfaEoCtavX49du3Zh9erV2LRp00A1d8A43n548cUXkZmZiYqKioFs5oATifGwYsUKrF27Fi+//DLuu+8+vPXWWwPV3AHlePsiFAph7dq1+Pa3v41169YhKSkp7uJXgMjNl4FAAC+88ALOPffcgWhmj5zwAnIwGMRXv/pVXHjhhTjnnHMAAKNGjcLevXsBAHv37kVmZiYAICcnx6MN3bVrF3JycvDss89ixowZSE5ORnJyMj7/+c/HlWO9IBJ9AQC//OUvsX79erz22mtgjGHKlCmDfCfHR1/6oSu66594IRL9MBSIVD+EO0+8EekxkZ6ejjlz5sSVGxYQmX5YuXIlXnjhBYwfPx6LFi3C66+/josuumjA2x5JIjUexNyYmZmJ+fPnY/Xq1QPX6AEiEn2Rm5uL3Nxc26KyYMECTwB8PBDJOeLll19GeXl51FxWT2gBmTGGyy67DPn5+fjRj35kHz/77LPx8MMPAwAefvhhzJs3zz7+yCOPgDGGVatWIS0tDaNHj8bYsWPx5ptvIhQKIRgM4s0334w7F4tI9YVhGDh48CAAYOPGjdi4cSPOOOOMwb+hftLXfuiKqqoqfPDBB/j4448RCASwdOlSnH322QPa9kgSqX6IdyLVD12dJ56IVF/s378fjY2NAIDW1la89tpryMvLG7B2R5pI9cOtt96KXbt24ZNPPsHSpUtx6qmn4tFHHx3QtkeSSPVDS0sLmpub7b9fffXVuMt4E6m+yMrKwpgxY7B9+3YA3P+2oKBg4BoeYSK9bjz++ONRc68AcGJnsXj77bcZAFZUVMRKSkpYSUkJe+mll9iBAwfYqaeeyiZNmsTmzp3LDh48yBjj0dff+c532IQJE9i0adPYmjVrGGM8ynLx4sUsLy+P5efnsx/+8IfRvK1+Eam+aG1tZfn5+Sw/P59Nnz6drVu3Lop31Xf62g979+5lOTk5LCUlhaWlpbGcnBw7a8dLL73EJk+ezCZMmMBuvvnmaN5Wn4lkPyxatIhlZWUxVVVZTk4Oe/DBB6N5a30iUv3Q1XniiUj1xYYNG1hpaSkrKipihYWF7MYbb4zynfWNSD4bgjfeeCPuslhEqh927NjBiouLWXFxMSsoKIi7uZKxyI6JdevWsYqKClZUVMTmzZvHDh06FM1b6xOR7IejR4+yYcOGscbGxqjdjyw1LZFIJBKJRCKRuDihXSwkEolEIpFIJJKOSAFZIpFIJBKJRCJxIQVkiUQikUgkEonEhRSQJRKJRCKRSCQSF1JAlkgkEolEIpFIXEgBWSKRSIYIN9xwA373u99FuxkSiUQS90gBWSKRSCQSiUQicSEFZIlEIoljfvOb32DKlCmYPXu2XYHr7rvvRkFBAYqLi7Fo0aIot1AikUjiDzXaDZBIJBJJ/2hoaMDSpUuxfv16hEIhlJeXo6KiArfddhs+/vhj+Hw+u6yzRCKRSHqP1CBLJBJJnPL2229j/vz5SExMRGpqKs4++2wAQHFxMS688EI8+uijUFWpB5FIJJK+IgVkiUQiGWK89NJL+O53v4u1a9eiqqoKoVAo2k2SSCSSuEIKyBKJRBKnnHLKKXjuuefQ2tqK5uZm/POf/4Rpmti5cyfmzJmD22+/HUeOHMHRo0ej3VSJRCKJK6TtTSKRSOKU8vJyLFy4ECUlJcjMzERVVRUIIbjoootw5MgRMMZw5ZVXIj09PdpNlUgkkriCMMZYtBshkUgkEolEIpHECtLFQiKRSCQSiUQicSEFZIlEIpFIJBKJxIUUkCUSiUQikUgkEhdSQJZIJBKJRCKRSFxIAVkikUgkEolEInEhBWSJRCKRSCQSicSFFJAlEolEIpFIJBIX/x+khZTl2oJVaAAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"markdown","source":"## [2. tsfresh](https://tsfresh.readthedocs.io/en/latest/index.html)\n[参考链接](https://tsfresh.readthedocs.io/en/latest/text/quick_start.html)","metadata":{}},{"cell_type":"code","source":"!wget http://mirror.coggle.club/lp1.data.txt\n    ","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:02.899895Z","iopub.execute_input":"2021-09-03T08:01:02.900380Z","iopub.status.idle":"2021-09-03T08:01:04.018387Z","shell.execute_reply.started":"2021-09-03T08:01:02.900321Z","shell.execute_reply":"2021-09-03T08:01:04.016593Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"--2021-09-03 08:01:03--  http://mirror.coggle.club/lp1.data.txt\nResolving mirror.coggle.club (mirror.coggle.club)... 47.246.22.184\nConnecting to mirror.coggle.club (mirror.coggle.club)|47.246.22.184|:80... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 27345 (27K) [text/plain]\nSaving to: ‘lp1.data.txt.1’\n\nlp1.data.txt.1      100%[===================>]  26.70K  --.-KB/s    in 0.08s   \n\n2021-09-03 08:01:03 (324 KB/s) - ‘lp1.data.txt.1’ saved [27345/27345]\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, load_robot_execution_failures\n\ntimeseries, y = load_robot_execution_failures(file_name=\"./lp1.data.txt\")","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:04.021288Z","iopub.execute_input":"2021-09-03T08:01:04.021877Z","iopub.status.idle":"2021-09-03T08:01:04.040232Z","shell.execute_reply.started":"2021-09-03T08:01:04.021822Z","shell.execute_reply":"2021-09-03T08:01:04.039028Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"timeseries.head()","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:04.042934Z","iopub.execute_input":"2021-09-03T08:01:04.043305Z","iopub.status.idle":"2021-09-03T08:01:04.063368Z","shell.execute_reply.started":"2021-09-03T08:01:04.043270Z","shell.execute_reply":"2021-09-03T08:01:04.062203Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"   id  time  F_x  F_y  F_z  T_x  T_y  T_z\n0   1     0   -1   -1   63   -3   -1    0\n1   1     1    0    0   62   -3   -1    0\n2   1     2   -1   -1   61   -3    0    0\n3   1     3   -1   -1   63   -2   -1    0\n4   1     4   -1   -1   63   -3   -1    0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>time</th>\n      <th>F_x</th>\n      <th>F_y</th>\n      <th>F_z</th>\n      <th>T_x</th>\n      <th>T_y</th>\n      <th>T_z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>62</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>61</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>3</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-2</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>4</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"timeseries[timeseries['id'] == 1]  # 样本 1","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:04.065081Z","iopub.execute_input":"2021-09-03T08:01:04.065413Z","iopub.status.idle":"2021-09-03T08:01:04.088387Z","shell.execute_reply.started":"2021-09-03T08:01:04.065383Z","shell.execute_reply":"2021-09-03T08:01:04.087218Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"    id  time  F_x  F_y  F_z  T_x  T_y  T_z\n0    1     0   -1   -1   63   -3   -1    0\n1    1     1    0    0   62   -3   -1    0\n2    1     2   -1   -1   61   -3    0    0\n3    1     3   -1   -1   63   -2   -1    0\n4    1     4   -1   -1   63   -3   -1    0\n5    1     5   -1   -1   63   -3   -1    0\n6    1     6   -1   -1   63   -3    0    0\n7    1     7   -1   -1   63   -3   -1    0\n8    1     8   -1   -1   63   -3   -1    0\n9    1     9   -1   -1   61   -3    0    0\n10   1    10   -1   -1   61   -3    0    0\n11   1    11   -1   -1   64   -3   -1    0\n12   1    12   -1   -1   64   -3   -1    0\n13   1    13   -1   -1   60   -3    0    0\n14   1    14   -1    0   64   -2   -1    0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>time</th>\n      <th>F_x</th>\n      <th>F_y</th>\n      <th>F_z</th>\n      <th>T_x</th>\n      <th>T_y</th>\n      <th>T_z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>62</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>61</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>3</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-2</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>4</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1</td>\n      <td>5</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1</td>\n      <td>6</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1</td>\n      <td>7</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>8</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>63</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>9</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>61</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1</td>\n      <td>10</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>61</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1</td>\n      <td>11</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>64</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1</td>\n      <td>12</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>64</td>\n      <td>-3</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>1</td>\n      <td>13</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>60</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>1</td>\n      <td>14</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>64</td>\n      <td>-2</td>\n      <td>-1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"timeseries[timeseries['id'] == 20].plot(subplots=True, sharex=True, figsize=(10,10))\nplt.show()  ","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:04.089794Z","iopub.execute_input":"2021-09-03T08:01:04.090097Z","iopub.status.idle":"2021-09-03T08:01:05.440454Z","shell.execute_reply.started":"2021-09-03T08:01:04.090068Z","shell.execute_reply":"2021-09-03T08:01:05.438418Z"},"trusted":true},"execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x720 with 8 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAl4AAAJACAYAAABc/WhtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABx5ElEQVR4nO3deXhcZfk+8Ps5Z9bsa5utbdLSQgulBVJ2kEUoyFIKgoCACvxQFlkUBUQBRbQqiBvKtyoCbiBIBQGtgCACsiSl0I2ldKHTJE06TbNPZnt/f8xJMpNMJtvMOTPJ/bmuXHPmbPPMm0nmnve8c44opUBEREREqadZXQARERHRVMHgRURERGQSBi8iIiIikzB4EREREZmEwYuIiIjIJAxeRERERCZJWfASkRki8qKIbBSRDSJynTH/XON+WERqU/X4REREROlGUnUeLxEpB1CulFojIrkA6gGcBUABCAP4PwA3KqXqRtpXSUmJqq6uTkmdRERERMlUX1+/WylVGm+ZLVUPqpRqBNBoTHeIyCYAlUqp5wBAREa9r+rqatTVjZjPiIiIiCwnItuHW2bKGC8RqQZwEIA3zHg8IiIionSU8uAlIjkA/grgeqVU+xi2u0JE6kSkrqWlJXUFEhEREZkkpcFLROyIhK4/KqWeGMu2SqmVSqlapVRtaWncw6REREREGSVlY7wkMojrtwA2KaV+nKrHISIioskhEAjA4/HA5/NZXcqouFwuVFVVwW63j3qblAUvAEcBuBjAOhFZa8z7BgAngJ8DKAXwjIisVUotTWEdRERElAE8Hg9yc3NRXV09pi/hWUEpBa/XC4/Hg5qamlFvl8pvNb4CYLhWW5WqxyUiIqLM5PP5MiJ0AZGzMxQXF2Os49B55noiIiJKG5kQuvqMp1YGLyIiIiKTMHgRERERGY488si48z//+c/j8ccfn/D+GbyIiIiIDK+99lpK95/KbzUSERERZZScnBx0dnZCKYUvf/nLeO655zBjxgw4HI6k7J/Bi4iIiNLOt/++ARsbRn3Bm1FZUJGH28/Yf1Trrlq1Cu+//z42btyIXbt2YcGCBbj00ksnXAMPNRIREREN8vLLL+OCCy6AruuoqKjACSeckJT9sseLiIiI0s5oe6YyDXu8iIiIiAY59thj8eijjyIUCqGxsREvvvhiUvbLHi8iIiKiQZYvX45///vfWLBgAWbOnIkjjjgiKftl8CIiIiIydHZ2Aoiclf4Xv/hF0vfPQ41EREREJmHwIiIiIjIJgxcRERGlDaWU1SWM2nhqZfAiIiKitOByueD1ejMifCml4PV64XK5xrQdB9cTERFRWqiqqoLH40FLS4vVpYyKy+VCVVXVmLZh8CIiIqK0YLfbUVNTY3UZKZXSQ40iMkNEXhSRjSKyQUSuM+YXichzIvKhcVuYyjqIiIiI0kGqx3gFAXxVKbUAwOEArhaRBQBuBvCCUmougBeM+0RERESTWkqDl1KqUSm1xpjuALAJQCWAZQAeMlZ7CMBZqayDiIiIKB2YNsZLRKoBHATgDQDTlVKNxqImANPNqmM43/77BmxsaLe6DCIiIkqhBRV5ll6A25TTSYhIDoC/ArheKRWTblTkO6NDvjcqIleISJ2I1GXKtxuIiIiIEpFUnytDROwAngawWin1Y2Pe+wCOU0o1ikg5gJeUUvsOt4/a2lpVV1eX0jqJiIiIkkFE6pVStfGWpfpbjQLgtwA29YUuw1MAPmdMfw7Ak6msg4iIiCgdpHqM11EALgawTkTWGvO+AWAFgL+IyGUAtgM4L8V1EBEREVkupcFLKfUKABlm8YmpfGwiIiKidMNrNRIRERGZhMGLiIiIyCQMXkREREQmYfAiIiIiMgmDFxEREZFJGLyIiIiITMLgRURERGQSBi8iIiIikzB4EREREZmEwYuIiIjIJAxeRERERCZh8CIiIiIyCYMXERERkUkYvIiIiIhMwuBFREREZBIGLyIiIiKTMHgRERERmYTBi4iIiMgkKQ1eIvKAiDSLyPqoeYtE5H8isk5E/i4ieamsgYiIiChdpLrH60EApwya9xsANyulFgJYBeBrKa6BiIiIKC2kNHgppV4GsGfQ7HkAXjamnwNwTiprICIiIkoXVozx2gBgmTF9LoAZ8VYSkStEpE5E6lpaWkwrjoiIiChVrAhelwK4SkTqAeQC8MdbSSm1UilVq5SqLS0tNbVAIiIiolSwmf2ASqn3AJwMACIyD8BpZtdAREREZAXTg5eITFNKNYuIBuCbAO4faZv6+vrdIrI99dWlnRIAu60uIs2xjRJj+yTG9hkZ2ygxts/IpmIbzRpugSilUvaoIvJnAMch0ui7ANwOIAfA1cYqTwC4RaWyiAwmInVKqVqr60hnbKPE2D6JsX1GxjZKjO0zMrZRrJT2eCmlLhhm0U9T+bhERERE6YhnriciIiIyCYNXeltpdQEZgG2UGNsnMbbPyNhGibF9RsY2ipLSMV5ERERENIA9XkREREQmYfAiIiIiMgmDFxEREZFJGLyIiIiITMLgRURERGQSBi8iIiIik5h+rcbxKCkpUdXV1VaXQURERDSi+vr63Uqp0njLMiJ4VVdXo66uzuoyiIiIKFMoBfR2AB2NkZ/2RqCjAbC5gCOuHnn7CRCR7cMty4jgRURERNQv6DcCVVMkTHU0Ae3GbXTQCnQN3Xb6ASkPXokweBEREVF6CIeBbu+gMBXdY2UErW7v0G11B5BbHvkpWwjMPXngfp5xm1sGOLLNf15RGLyIiIgo9Xo7jPDUOChMNUb1XjUB4cCgDQXILo2Ep/xKoKoWyKuIhKhc4zavAnAXAiKWPLWxyNjgFQgE4PF44PP5rC4lpVwuF6qqqmC3260uhYiIaKigH+jcFT9MRR/+83cO3daZN9ATVX300DCVWwbkTAf0yfMemLHBy+PxIDc3F9XV1ZAMSLjjoZSC1+uFx+NBTU2N1eUQEdFUEg4DPXuiwtPgsVTGbVfL0G01+0Cgmr4A2OeTUWEq6rCfM8f852WxjA1ePp9vUocuABARFBcXo6UlzouaiIhovHo7B8JT3MN/Ri/VkMN+iBz2yy2P9ExVHhIVpKLGUrmLAI2nCo0nY4MXgEkduvpMhedIRERJEgpEDvuNNJaqt33oto5co1eqHJh1hBGkBo2lypkO2BzmP69JJKODl9X27t2LP/3pT7jqqqvQ0NCAa6+9Fo8//rjVZRER0WSjFNC9J06YGnT4r6sFgIrdVrMb4akcKN0PmHNC/LFUzlxLntpUw+A1AXv37sUvf/lLXHXVVaioqGDoIiKisfN3xR87Nfh+yD9026ySgcN7FQcNOnWC8ZNVzMN+aYTBawJuvvlmfPTRR1i8eDHmzp2LTZs2Yf369XjwwQfxt7/9DV1dXfjwww9x4403wu/34/e//z2cTieeffZZFBUV4aOPPsLVV1+NlpYWZGVl4de//jX2228/q58WERElQyg4/Lf9osdS9bYN3daePRCgZhw+NEzllRuH/ZzmPy+akMkRvP5xM9C0Lrn7LFsInLoi4SorVqzA+vXrsXbtWmzbtg2nn356/7L169fj7bffhs/nwz777IMf/OAHePvtt3HDDTfg4YcfxvXXX48rrrgC999/P+bOnYs33ngDV111Ff79738n93kQEVFyKQX0tCYIU0YPVWczhh72swE5xjiq0nnA7ONiD/f1Hf5z5VnxzMgEkyN4paHjjz8eubm5yM3NRX5+Ps444wwAwMKFC/Huu++is7MTr732Gs4999z+bXp7e60ql4iIAMDfHXUyzwRjqUJx/l9nFUedOf3A2DDV12OVVcLDflNcSoOXiDwA4HQAzUqpA4x5RQAeBVANYBuA85RSrRN6oBF6pqzgdA50/2qa1n9f0zQEg0GEw2EUFBRg7dq1FlVIRDSFhIKRgedDLkUzaCyVL95hv6yBb/jNODTOZWiMc1LxsB+NQqp7vB4E8AsAD0fNuxnAC0qpFSJys3H/phTXkRK5ubno6OgY17Z5eXmoqanBY489hnPPPRdKKbz77rtYtGhRkqskIprElAJ8exNf16+jKTLWSoVjtxU9Mk4qrxwonhM5c3q8sVTOvIy4FA1lhpQGL6XUyyJSPWj2MgDHGdMPAXgJGRq8iouLcdRRR+GAAw7A/Pnzx7z9H//4R1x55ZX47ne/i0AggPPPP5/Bi4ioT8CX+FxUfT1VwZ6h27oLB8ZLTd8//kk+s0sBTTf/edGUJkqpkdeayANEgtfTUYca9yqlCoxpAdDad3/QdlcAuAIAZs6cecj27dtjlm/atGlcYScTTaXnSkRTQDhkHPZL8E2/jobIAPbBbK5BJ/Yc5rCf3W3+8yIyiEi9Uqo23jJLB9crpZSIxE1+SqmVAFYCQG1tbWrTIRERTZxSkTFScc9FFRWsOncBKhS7rWiRw365ZUBhNTBzmFMouAp42I8ymhXBa5eIlCulGkWkHECzBTUQEdFYBHvjD0bvaIr91l+ge+i2roKB4FS630CvVPS3/rJLAZ1ftKfJz4pX+VMAPgdghXH7pAU1EBERAITDA4f9Eo2l6tkzdFvdOdArVb4YmBfnkF9uOeDIMv1pEaWrVJ9O4s+IDKQvEREPgNsRCVx/EZHLAGwHcN5496+UmvQXkU71GDwimqSUAno7Ep+LqsM47BcODtpYBg77Fcw0TqHQd12/qGDlLuRhP6IxSvW3Gi8YZtGJE923y+WC1+tFcXHxpA1fSil4vV64XC6rSyGidBLsNYLTCGOpAl1Dt3XmG+GpDCj5RNQhv6hxVNnTeNiPKEUy9i+rqqoKHo8HLS0tVpeSUi6XC1VVVVaXQURmCIeB7t0jf9uv2zt0W90Rddb0hcDck+N8468McGSb/7yIqF/GBi+73Y6amhqryyAiGp3ejmHCVNRYqo4mIBwYtKFEBp7nlQP5VUBV7dDr+uVV8LAfUYbI2OBFRJQWgv7IOKm4YSrqvr9z6LbOvIGeqOqjh4ap3LLIWCvdbv7zIqKUYPAiIopHqcghvURhqqMx8o3AwTT7wCG+6QuAfT459PQJuWWAM8f850VElmLwIqKpx9+VOEy1NwKdTUDIP3Tb7NKB8FRxUGyY6htL5S4CNM3850VEaY/Bi4gmj1AA6Gw2wtPgk3xGfeuvt33oto6cgcN+s44YGqZyyyOH/WwO858XEU0aDF5ElP6Uily3b9gwZdx2NgMYdO47zQbkGOefKp0HzD5u6GG/vHLAmWvFMyOiKYbBi4is5e8eeWB6RxMQ6h26bVbxwHip8kVDr+uXWw5klfCwHxGlDQYvIkqNUBDoah7hFAqNkYsqD2bPHghOMw6LPzA9twywOc1/XkREE8DgRURj03fYL96JPaMP/3U1Ayocu63oA9fvK94HqDk2zikUjMN+PCcVEU1CDF5ENCDQM/xFkqPDVdA3dFt30UCvVNnCQWdMN36ySwBNN/95ERGlCQYvoqkgHDK+7Zfgun4djYBv79BtbW4jQFUAlbUD0zEn+SwD7LymKBHRSBi8iDKZUpGwFK9Xqr0x6tt+u+If9suZHglSxXMGzpw+eCyVK5+H/YiIkoTBiyhdBXxR1/CL7qkadPgv2DN0W3fhwDmppu0fe8ivbzq7lIf9iIhMxuBFZLZwCOjaHf9cVNED1Xtah25rcxnhqQKoPHhomOoLW3a3+c+LiIhGxOBFlCxKRc6IHjdMRQ1U72gCVCh2W9GA7GmRAFVYDcw8fOjA9LxywFXAw35ERBmMwYtoNIK98QejDz7sF+gauq0rfyA8le43cDqFmMN+0wCdf45ERJMd/9PT1BYOA927E5w13ei16vYO3VZ3DgxGLz8QmLd04DBgf7gqAxzZ5j8vIiJKSwxeNHn52hNf16+9EehsAsLBQRsKkDMtEpryq4Cq2oETe0b3UrkLediPiIjGhMGLMk/QHwlMI42l8ncO3daZb/RSlQM1x8QfnJ4zDdDt5j8vIiKa9Bi8KH2Ew5FDeomu69feGDk0OJjuGDi8N/0AYJ+TBp1CoSJyzipnjvnPi4iIyMDgRebo7TSC0+DDfdFnUG8CwoFBG0rkfFO5ZUBeJVB5SNRFkssHzqKeVcTDfkRElPYYvGhiQoGBUyQkGkvl7xi6rSPXCE5lwKyj4p8+IWc6D/sREdGkweBF8SkFdO+JM3Zq0OG/rt0AVOy2mj3qrOnzgTknxBlLVQY4cy15akRERFZh8JqK/F2Jw1TfYb+Qf+i2fYf9ciuAioOGXtcvrwJwFwGaZv7zIiIiSnMMXpNJKBi5GHKigekdTUBv29BtHTkDPVEzjxgapnLLgJwywOYw/3kRERFNEgxemUCpyHX7hg1Txk9nM4Ye9rNFAlNeOVA6D5h9XGyYyq2ILONhPyIiopRj8LJaoGfQN/uGOewX9A3dNqt4YOxU2cL4h/2ySnjYj4iIKE1YFrxE5BQAPwWgA/iNUmqFVbWkRCgIdLUM+qbfoOv6dTQAvjiH/exZA4Gqakmcw37GIUGb0/znRURERONmSfASER3AfQBOAuAB8JaIPKWU2mhFPWOiFODbm/i6fh1NkbFWKhy7reiR0yPklQPFc4Dqo+OfQsGZx3NSERERTUJW9XgdCmCzUmoLAIjIIwCWAbA2eAV6hj/k1x592K9n6LbuwoFeqen7Dw1TueWRbwRquvnPi4iIiNKCVcGrEsCOqPseAIdZVEukF+ue/SLX/xvM5hq45EzlwQPT0Yf/cssBu8v8uomIiCijpO3gehG5AsAVADBz5sxUPxhw0EWR8DT4FAquAh72IyIioqSwKnjtBDAj6n6VMa+fUmolgJUAUFtbO+gcCSlw4rdS/hBEREQ0tYlSqc80Qx5UxAbgAwAnIhK43gJwoVJqwzDrtwDYbl6FaaMEwG6ri0hzbKPE2D6JsX1GxjZKjO0zsqnYRrOUUqXxFljS46WUCorINQBWI3I6iQeGC13G+nGLn+xEpE4pVWt1HemMbZQY2ycxts/I2EaJsX1GxjaKZdkYL6XUswCeterxiYiIiMzGU5oTERERmYTBK72ttLqADMA2SoztkxjbZ2Rso8TYPiNjG0WxZHA9ERER0VTEHi8iIiIikzB4EREREZmEwYuIiIjIJAxeRERERCZh8CIiIiIyCYMXERERkUksO3P9WJSUlKjq6mqryyAiIiIaUX19/e60ulbjWFVXV6Ouri6lj+Hp8CDXkYt8Z35KH4eIiIgmNxHZPtwyHmoE0OHvwGee/gxWvLnC6lKIiIhoEmPwApDryMVF8y/C01uexr+2/cvqcoiIiGiSYvAyXH7g5Tig+ADc+fqdaOlusbocIiIimoQyYoyXGeyaHXcdcxfO+/t5uP2123HfifdBRKwui4iIaFIJBALweDzw+XxWlzJhLpcLVVVVsNvto96GwSvK7PzZuOGQG7DizRX464d/xafnfdrqkoiIiCYVj8eD3NxcVFdXZ3QHh1IKXq8XHo8HNTU1o96OhxoHuWC/C3BY+WH44Vs/xI72HVaXQ0RENKn4fD4UFxdndOgCABFBcXHxmHvuGLwG0UTDd4/6Lmxiw62v3opQOGR1SURERJNKpoeuPuN5HgxecZRll+GWw27B281v48END1pdDhEREU0SDF7DOH326Thp1kn4xdpf4P0971tdDhERESWJrutYvHhx/8+2bdtMe2wOrh+GiOBbh38La3atwTde+Qb+fNqf4dAdVpdFREREE+R2u7F27VpLHpvBK4FCVyG+c9R3cPULV+O+tffhhkNusLokIiKiSeMHb/4A7+15L6n73K9oP9x06E1J2de9996LdevW4YEHHsC6detwwQUX4M0330RWVta498lDjSM4tupYnDP3HPxu/e+wZtcaq8shIiKiCerp6ek/zLh8+fJh17vuuuuwefNmrFq1Cl/4whfwf//3fxMKXQB7vEbla0u+htcbX8etr9yKx898HNn2bKtLIiIiynjJ6pkaq9EeatQ0DQ8++CAOPPBAfPGLX8RRRx014cdmj9coZNuz8b2jv4ednTtxd93dVpdDREREJvnwww+Rk5ODhoaGpOyPwWuUDp5+MD5/wOfx+AeP42XPy1aXQ0RERCnW1taGa6+9Fi+//DK8Xi8ef/zxCe+TwWsMrll8DeYWzsVtr96GVl+r1eUQERFRCt1www24+uqrMW/ePPz2t7/FzTffjObm5gntk8FrDBy6A98/+vto87fhztfvhFLK6pKIiIhojDo7O0e13gMPPIBrr70WADBjxgxs3rwZ06ZNm9BjM3iN0b5F++LqxVfjue3P4Zmtz1hdDhEREWUQBq9x+ML+X8Di0sX43uvfQ1NXk9XlEBER0QSsXr065kz2I51mYiJ4Oolx0DUd3zv6ezjn7+fgm69+EytPWglNmGGJiIgy0dKlS7F06VJTHispaUFEHhCRZhFZHzWvSESeE5EPjdtCY76IyM9EZLOIvCsiByejBrPNyJuBry35Gt5ofAOPvPeI1eUQERFljMkyRno8zyNZ3TQPAjhl0LybAbyglJoL4AXjPgCcCmCu8XMFgF8lqQbTfXrup3FM5TG4t/5ebG3banU5REREac/lcsHr9WZ8+FJKwev1wuVyjWk7SdYTF5FqAE8rpQ4w7r8P4DilVKOIlAN4SSm1r4j8nzH958HrDbfv2tpaVVdXl5Q6k62luwXLn1qOGTkz8PtP/R42jUdviYiIhhMIBODxeODz+awuZcJcLheqqqpgt9tj5otIvVKqNt42qUwJ06PCVBOA6cZ0JYAdUet5jHnDBq90VppVim8d/i3c+J8b8et1v8aVi660uiQiIqK0ZbfbUVNTY3UZljFlRLiKdKuNqWtNRK4QkToRqWtpaUlRZcmxtHopTpt9Gla+sxIbdm+wuhwiIiJKU6kMXruMQ4wwbvtO9boTwIyo9aqMeTGUUiuVUrVKqdrS0tIUlpkctxx6C4rcRbjllVvgC2Z+9ykRERElXyqD11MAPmdMfw7Ak1HzLzG+3Xg4gLZE47syRb4zH3cedSe2tm3FT9f81OpyiIiIKA0l63QSfwbwPwD7iohHRC4DsALASSLyIYBPGvcB4FkAWwBsBvBrAFclo4Z0cGTFkbhgvwvwh01/wBuNb1hdDhEREaWZpH2rMZXS+VuNg/UEe3De38+DL+TDE2c+gVxHrtUlERERkYkSfauRp1tPMrfNje8d/T20dLdgxZsrRt6AiIiIpgwGrxRYWLoQly+8HE999BRe2P6C1eUQERFRmmDwSpEvLvoi5hfNx7f/923s7tltdTlERESUBhi8UsSu2fH9Y76PrkAXvv3atzP+0ghEREQ0cQxeKTSnYA6uP+R6vOR5CX/b/DeryyEiIiKLMXil2GfnfxZLypZgxZsr4OnwWF0OERERWYjBK8U00fDdo74LTTTc+sqtCIVDVpdEREREFmHwMkFFTgVuPvRmrGleg99v/L3V5RAREZFFGLxMcuacM3HCjBPws7d/hg9bP7S6HCIiIrIAg5dJRAS3H3k7ch25uOW/tyAQClhdEhEREZmMwctERa4i3HHEHXi/9X386p1fWV0OERERmYzBy2THzzwey/dZjt+u/y3WNq+1uhwiIiIyEYOXBb6+5Osozy7Hra/ciu5At9XlEBERkUkYvCyQ48jBnUfdiR0dO/Dj+h9bXQ4RERGZhMHLIkvKluCSBZfg0fcfxSs7X7G6HCIiIjIBg5eFvnzwl7FPwT647dXb0NbbZnU5RERElGIMXhZy6k587+jvodXXiu++/l2ryyEiIqIUY/Cy2Pzi+bhy8ZX457Z/4h9b/2F1OURERJRClgUvETlFRN4Xkc0icrNVdaSDSw+4FAeWHog7X78Tu7p2WV0OERERpYglwUtEdAD3ATgVwAIAF4jIAitqSQc2zYbvHf09BMNB3PbabVBKWV0SERERpYDNosc9FMBmpdQWABCRRwAsA7DRonosNytvFr56yFfx3Te+i0fffxTn73e+1SXRKATCAbT1tmGvby/a/G0IhUNWl0RjJCIQCDTRoIkGEYGGqGnRIJC48zVETRvr9S0faZ8aNEAQMz+dKaUQVmGEER6YVmEoRE0r1b883vz+aWM/UBi6zzj777uvEPlQ6tSdcOiOyI/mGHJf13SLW4ui9f3+QiqEYDgYMx1SIYTCocitMR1UQYTCIYRVuH86ev2wCsdsG1TGPqO2Hbzf6PULnAW4ZP9LLGsPq4JXJYAdUfc9AA6LXkFErgBwBQDMnDnTvMosdN6+5+HFHS/inrp7cETFEZiVNyvpjxFWYXT4O9Dub4/89EZu23rbhswbPO0P+ZFjz0GeMw95jjzkO/OR54hM982Lvp/vyO+f77K5kv5ckkkphe5gN/b27sXe3r1o87UNTPfGn97buxddgS6rS6dJpC+8pYu+0NMXeDKFTWxw6JFAZtftcOrOyLRmjwlpTt0Jh+aIvR9nWaL92DV7f8iMDonDhdK+6f5t4gXOqOnx7HO8gST6Nib8DBOQYrYftI/Bj50udNExp2COpcFLrDisJSKfBnCKUupy4/7FAA5TSl0Tb/3a2lpVV1dnZomW2dW1C2c/dTaq86vx0CkPwaYNzcZhFUZnoDMmFPUHp2FCU9/yTn9nwn+ids0+JEj1BSyn7hzYd/T+e9vREehI+LwcmiPuPmMC2zCBzqk7x9SGwXAQ7f72gaDkix+aoue19bYhEB7+wuW59lzkO/NR4CxAvityW+gs7J9X4CxAnjMPds0+plrJekPeBOP0zIzYSxPnDTH6zThRz0//GyvCadljGt1jF693b6w9ff3rRm0zlt7DsArDH/JHfsJ+9IZ6++/3hnrhD0dNR603eF70uvHWn0xsYoOu6dDF+NEGbm1igyYabJotdlnU9HDL+vYbs72xviba0O3Hus+ouvv3OWjbvvqjtx3usczsWRaReqVUbdzfhykVDLUTwIyo+1XGvClvevZ0fPPwb+LrL38d1/z7GuTYc4aEqs5AJ8IqPOw+bJotJrgUuYpQk18zpEcqXu+US3eN64UZCodiwmCbv21IOIsObM3dzdi8dzPaetvQGehMuG+n7oxba5YtC52BziGhqsM/fAi0iQ0FrkhQynfmY1beLCxyLooJUIOn8535cQMwEU1OSikEwgH0hnrRG+pFIBQ1bczvC2mBcGBo4BxFqEwUSocEzgShdMh9aDGBJBMOY081Vr2bvAVgrojUIBK4zgdwoUW1pJ1Ta07Fuy3v4tmtz/YHjkJXIarzqoftJYruKXLb3Kb/oema3h9SxioYDqLT35mwpy56flN3Ez5o/QCdgU7kOgZ6oqpyq+KGp+j72fZs/hMiooREpP+QYy5yrS6HJhlLDjUCgIh8CsBPAOgAHlBK3TXculPpUCMRERFltnQ81Ail1LMAnrXq8YmIiIjMZlmP11iISAuA7VbXYYESALutLiLNsY0SY/skxvYZGdsoMbbPyKZiG81SSpXGW5ARwWuqEpG64boqKYJtlBjbJzG2z8jYRomxfUbGNorFazUSERERmYTBi4iIiMgkDF7pbaXVBWQAtlFibJ/E2D4jYxslxvYZGdsoCsd4EREREZmEPV5EREREJmHwIiIiIjIJgxcRERGRSRi8iIiIiEzC4EVERERkEsuu1TgWJSUlqrq62uoyiIiIiEZUX1+/e7hLBmVE8KqurkZdXV1KH0MpBRFJ6WMQERHR5Cciw15fOimHGkXkARFpFpH1UfOKROQ5EfnQuC005ouI/ExENovIuyJycDJqmIhwVxc813wZbU8+aXUpRERENIkla4zXgwBOGTTvZgAvKKXmAnjBuA8ApwKYa/xcAeBXSaph3MTpRLi9HY13fBu9H35odTlEREQ0SSUleCmlXgawZ9DsZQAeMqYfAnBW1PyHVcTrAApEpDwZdYyX2GyouOduaFlZ8Fx/A8JdXVaWQ0RERJNUKsd4TVdKNRrTTQCmG9OVAHZErecx5jVGzYOIXIFIjxhmzpyZwjIj7NOmofKeu/HxFy5F4x3fRsUPf8AxX0REREkWCATg8Xjg8/msLmXCXC4XqqqqYLfbR72NKYPrlVJKRMZ0UUil1EoYF9asra015YKS2YcfjpIvX4PdP/s5spbUovC888x4WCIioinD4/EgNzcX1dXVGd3BoZSC1+uFx+NBTU3NqLdL5Xm8dvUdQjRum435OwHMiFqvypiXFkq+9CVkH3UUdn33Lvg2bbK6HCIioknF5/OhuLg4o0MXAIgIiouLx9xzl8rg9RSAzxnTnwPwZNT8S4xvNx4OoC3qkKTlRNNQ8aMfQi8shOf66xHq6LC6JCIiokkl00NXn/E8j2SdTuLPAP4HYF8R8YjIZQBWADhJRD4E8EnjPgA8C2ALgM0Afg3gqmTUkEy2oiJU/vgeBDw70fjNb0EpU450EhER0SSXlDFeSqkLhll0Ypx1FYCrk/G4qZR1yCGY9pUb0Pyju9H6hz+i6OKLrC6JiIiIMhyv1ZhA0Re+gJzjj8euH/4QPe++a3U5RERElAS6rmPx4sX9P9u2bTPtsTPikkFWEU1Dxfe/h61nn4Od19+Amif+Cr2gwOqyiIiIaALcbjfWrl1ryWMzeI1ALyhA5U/uxbbPXoSGm29B1S/vg2jsKCQiIpqopu99D72b3kvqPp3z90PZN76RlH0de+yx+NnPfobFixcDAI4++mjcd999WLRo0bj3yQQxCu4DD8T0r38dnS+9hD0PPGB1OURERDQBPT09/YcZly9fPux6l112GR588EEAwAcffACfzzeh0AWwx2vUCi/6LLrr69F870/gXrwYWbW1VpdERESU0ZLVMzVWoz3UeO655+LOO+/Ej370IzzwwAP4/Oc/P+HHZo/XKIkIyr97J+xVldj5la8i6PVaXRIRERGlUFZWFk466SQ8+eST+Mtf/oLPfvazE94ng9cY6Dk5qPrpTxHauxcNX/saVChkdUlERESUQpdffjmuvfZaLFmyBIWFhRPeH4PXGLn22w/Tv/VNdL32P+y+/36ryyEiIqIUOuSQQ5CXl4cvfOELSdkfg9c4FHz608hfdiZ2/+I+dP3vf1aXQ0RERGPQ2dk56nUbGhoQDodx8sknJ+WxGbzGQURQdvvtcMyZjZ03fg2BXc0jb0REREQZ5eGHH8Zhhx2Gu+66C1qSTiXFbzWOk5aVhaqf/ARbzz0PDV/9KmY++DuIjc1JRESUaVavXo2bbropZl5NTQ1WrVqFSy65JKmPxaQwAc599kH5t+9Aw9dvQsvPfo5pX7nB6pKIiIjSnlIKImJ1Gf2WLl2KpUuXjnm7yOWnx4aHGico/8wzUXDuufCuXInO//zH6nKIiIjSmsvlgtfrHVdoSSdKKXi9XrhcrjFtJ5nwxGtra1VdXZ3VZQwr7PNh2/kXINjYiJpVT8BeUWF1SURERGkpEAjA4/HA5/NZXcqEuVwuVFVVwW63x8wXkXqlVNwzrTN4JYl/2zZsPefTcOwzB9W//z3E4bC6JCIiIrJAouDFQ41J4qiuRvldd8H3zrtovuceq8shIiKiNMTglUR5pyxF4cUXY89DD6P9X/+yuhwiIiJKMwxeSTb9azfCdeCBaPzGrfB//LHV5RAREVEaYfBKMnE4UPnjHwO6Ds/11yPc22t1SURERJQmGLxSwFFViYoV30fvxk3Y9f3vW10OERERpQkGrxTJPf54FF9+GfY+8ija/v601eUQERFRGmDwSqHS666D+5BD0Hj77ejdssXqcoiIiMhiDF4pJHY7Kn98DzSnEzuvuw7hnh6rSyIiIiILMXilmH36dFT86Efo3fwRmr5zp9XlEBERkYUYvEyQc/RRKLnySrStWoW9f33C6nKIiIjIIgxeJim5+ipkHX44mr7zHfjef9/qcoiIiMgCDF4mEV1H5d0/gp6Xh53XXodQZ6fVJREREZHJGLxMZCspQeWP74F/xw403XYbMuEC5URERJQ8DF4my1qyBKXXX4/2Z/+B1j//2epyiIiIyEQMXhYovvwyZH/iWDR/fwV61q23uhwiIiIyCYOXBUTTULFiBfSSEuy8/nqE2tqsLomIiIhMwOBlEVthIaru/TECu3ah4Ru3crwXERHRFMDgZSH34sWY/rUb0fnCC9jz4ENWl0NEREQpxuBlscJLLkHuSZ9E8z33oHvN21aXQ0RERCnE4GUxEUH5XXfBXl6OnV/5CoKtrVaXRERERCnC4JUG9Lw8VP7kXoS8XjR87etQ4bDVJREREVEKMHilCff++2P6rd9A1yuvwLtypdXlEBERUQoweKWRgs98BnmnnYaWn/0cXa+/YXU5RERElGQMXmlERFD+nW/DUV2NnTfeiGBLi9UlERERURLZrHpgETkFwE8B6AB+o5RaYVUt6UTLzkblT+7FtvM+gy2nnwFHdTXslRWwlZfDXl4Be0UF7BXlsFdUQMvNhYhYXTIRERGNkiXBS0R0APcBOAmAB8BbIvKUUmqjFfWkG9e8eZhx/6/Q9tTfEWhsQM+GDQg+9zxUIBCznpadDXtFOWwVFbDHCWa20lKIzbJsTRZToRAgAtHYsU1jp5QCAgEoAKLrgKbxg14aU0oBoRBUOBz5Pek6//ajKKWAcDjSRkpBczotq8Wqd+VDAWxWSm0BABF5BMAyAAxehuzDD0f24Yf331fhMEJeLwKNjQg0NCLQ0GBMNyDQ2ADfO+8itHdv7E50Hbbp0yJhrC+UlZf3BzN7eTm07Gxznxj1v6GFfT6Ee3xQvh6EfT6onshtuLsnMq/Hh7CvByp6vW5j3ejlPb6B7XsG9qUCAUDToBcUQC8shF5YAFthkTFdCFtRYf+0XjBwX9xuvsGaTCkF5fdHfoe9fqhAIHI/4Ddujfv+2PvhmPuBIctj7gei1vcHht1n9P0hNC0SwnR94FbTAJvNuNUhmg7oGkS3QXQN0PRhtjHWjbeNbuyvf5vIMugaRDQgnV6fSkU+5IRDUKEwVCgIhMKR+8EQVDgEBCOBCKFQZF0jICEYjJk/sG4ICIUTrjuwTWQe4n0bvi+ARbV733R/e0f//ob7HSX83Yz8+4yEwoG2iXleoSBU3PZK1AbGNkPaLXEb93HMmYM5zzxt4oskllXBqxLAjqj7HgCHRa8gIlcAuAIAZs6caV5laUo0DbbSUthKS+E+8MC464S7uhBoaoqEsYZGBBobEGhoQLChET319Wh/9tmYFx8A6Pn5Az1mfbeVkVtbeTlsJSUT+tSklIq88KPeKBAIGG8Agdh/+HHeZGLXH/SGEe+NwUrGm2ckUHXHBiJfVEjq6RnyexgNcTqhuVwQtztym+WG5nJDy8qCXlxsLHNF5rndEJcTKhhEaE8rQq2RH/+2rQiuWRMJ6cPUIE4n9KKiSFArMIJZ3/2+oFYYdb+gAGK3T7Dx0pcKhyPhN/p32TM4/PbEmecb+bXgi7weVE8PkKzLhuk6xOGI/NjtEIcdmj36fmRac7tj7ovD3n9fi9oeIsYbWXggYASHBo2YN8zh3gSj3zB9vQiP4g2zP8j0LU/H0+3YhgswGkTTITY9NoBGrSsOx9BwGbONEXbihlhtaNjRdECFhw+BcX9nwwSgmLAUhOpNEALDwwSrYDDS854osOvxn5foOsRmgzgdA4FvmDbua4PE7RZpY72o0NqXi6WPnoBSaiWAlQBQW1vLCxmOgpadDeecOXDOmRN3uQqFEGxpiRvMAjt2oPuNNxDu6orZRux2Y3xZOWzFRVCBYOwn7kBfGPIP+4k7aW8owMCbit3e/6aQTjSHYyAYuV3Q8/KgTZ8eFYhckP5bFzR3VtS0sZ2xXHO7B/blciX1sIEKhxHu6ECotRXBPa0I7R0IZ8GooBZs3QO/x4NQayvCHR3DP++8vIGgVlQ00MNWVAS9L7wVFkDPyYn/qd14Q+1/Q496Y+97Q4h5s4jZPhy7Tdw3jb43gqhtQkGoYAjhXh9U99BA1BeSlM839gbWtJjfX//v3eWCXlQIu7syJij3vz5cTojTCekLSjFhyQ7N4QDs9oFgNChMid0eeYMhorRlVfDaCWBG1P0qYx6lkOg67GVlsJeVAQfHXyfU0WEEMyOURR3a9G3YaHwqHvgnr+fkxn2DiL6vDV5ud8TuJ+qTef/6MZ/Eo+7zTSUpRNOg5+dDz8+Ho7p6VNsovx/BvXsRat0bCWZ7WxHcs8cIaca81j0INDXBt2kTQnv2RIK3FUQGehTiHhIbODSmudz9PYn2vLxICHJnDepBHEVgjroVu52Ha4koLquC11sA5opIDSKB63wAF1pUC0XRc3Oh77svXPvua3UplGbE4YB92jTYp00b1fpKKajubgSjglq4s3PgEIGmRb78YYSjmEMNgw8VRM+3DWwb/zCFztBDRGnLkuCllAqKyDUAViNyOokHlFIbrKiFiFJDRCDZ2XBkZwNVlVaXQ0SUFkQlc/xNiohIC4DtVtdhgRIAu60uIs2xjRJj+yTG9hkZ2ygxts/IpmIbzVJKlcZbkBHBa6oSkTqlVK3VdaQztlFibJ/E2D4jYxslxvYZGdsoFs+uRkRERGQSBi8iIiIikzB4pbeVVheQAdhGibF9EmP7jIxtlBjbZ2Rsoygc40VERERkEvZ4EREREZmEwYuIiIjIJAxeRERERCZh8CIiIiIyCYMXERERkUkYvIiIiIhMYslFsseqpKREVVdXW10GERER0Yjq6+t3D3etxowIXtXV1airq0vpY3y0phlls/ORXeBM6eMQERHR5CYi24dblhHBK9V6uwN48Q/vwWbX8KmrDsS0WXlWl0RERESTEMd4AXBm2XHWVw6GpmtYdfcafFi3y+qSiIiIaBJi8DKUVOXg0zfXonRmLv71mw148+mt4OWUiIiIKJl4qDFKVp4Dy64/CC/98T289fRWtDZ24YTPzYfdoVtdGhER0aQRCATg8Xjg8/msLmVCXC4XqqqqYLfbR70Ng9cgul3DCZ+bj6KKHLy2ajPaWnrwqSsPRE4hB90TERElg8fjQW5uLqqrqyEiVpczLkopeL1eeDwe1NTUjHo7HmqMQ0Rw0MkzcdqVB2Lvrm48tuIt7NrWbnVZREREk4LP50NxcXHGhi4gkhWKi4vH3GvH4JVA9YElOOfrh0C3aVh1zxp8+BYH3RMRESVDJoeuPuN5DgxeIyiuzMG5N9di2qxc/Ou3G/DGU1ugwhx0T0RERGPH4DUK7tzIoPv5R5aj7tltWP3r9Qj0hqwui4iIiMZJ13UsXry4/2fbtm2mPG5SBteLyAMATgfQrJQ6wJhXBOBRANUAtgE4TynVKpF+uZ8C+BSAbgCfV0qtSUYdqaTbNBx/8X4oqsjGa3/djLa763HaVQcip9BldWlEREQ0Rm63G2vXrjX9cZP1rcYHAfwCwMNR824G8IJSaoWI3GzcvwnAqQDmGj+HAfiVcZv2RASLPzkTBdOz8K/fbsBj36/DqVcuRFlNvtWlERERZaT//uUD7N7RmdR9lszIwTHnzUvKvi6//PL+yxbu3LkT11xzDW6//fZx7y8phxqVUi8D2DNo9jIADxnTDwE4K2r+wyridQAFIlKejDrMUr0wMuje5tDwt3vexgdvNlldEhEREY1BT09P/2HG5cuXD7veb37zG6xduxZPPvkkSkpK8PnPf35Cj5vK83hNV0o1GtNNAKYb05UAdkSt5zHmNSKDFFdEznT/z/9bj+ce2Ig9jV047IzZEC3zv6VBRERklmT1TI3VWA41+nw+nHvuufj5z3+OWbNmTehxTRlcryLX3hnTVwFF5AoRqRORupaWlhRVNjHuHAfOvG4xFhxVjvp/bMc/V66H3xe0uiwiIiJKoi996Us4++yz8clPfnLC+0pl8NrVdwjRuG025u8EMCNqvSpjXgyl1EqlVK1Sqra0tDSFZU6MbtNw3EX74ehz52LrOy144u416NiT2ZdAICIiooj77rsPHR0duPnmm5Oyv1QGr6cAfM6Y/hyAJ6PmXyIRhwNoizokmZFEBItOnIHTrl6Ejt09eGxFHZq2tFldFhEREU3Q3XffjXXr1vWPB7v//vsntL9knU7izwCOA1AiIh4AtwNYAeAvInIZgO0AzjNWfxaRU0lsRuR0El9IRg3pYNYBxTjnplo888t38bcfv43jL94P+x5WZnVZRERENEhn5+i+Sbl169akPm5SgpdS6oJhFp0YZ10F4OpkPG46KirPxrk31eKfK9fh+d9txJ6GLhy+jIPuiYiIKLXfapyyXDl2nHHtYrz86AdYs3o7Wpu68MkvLIDDxeYmIiJKR6tXr8ZNN90UM6+mpgarVq1K6uMwCaSIbtNw3IX7oqg8G68+9iGe+NEafOqqhcgrdltdGhEREQ2ydOlSLF26NOWPw2s1ppCIYNEJM3D6lxehY48Pj6+oQ+NHHHRPREQUGXmU2cbzHBi8TDBzQTE+fdMhcLhs+Nu9a/De6xn9JU4iIqIJcblc8Hq9GR2+lFLwer1wucZ2zWYeajRJYVl25Ez3K9fjhQc3RQbdnzUHGgfdExHRFFNVVQWPx4N0PUH6aLlcLlRVVY1pGwYvE7my7Tjj2kX476Mf4u1/fYzWpm6cdCkH3RMR0dRit9tRU1NjdRmW4KFGk+l6ZND9sefPw/b1Xvz1h/Vo391jdVlERERkAgYviyw8rgpnXLMIna29eGxFHRo277W6JCIiIkoxBi8LzVhQhE/fdAicWTY8ee/b2PQaB90TERFNZgxeFissy8anb6pFxdwC/PvhTXj18Q8RDmfutzyIiIhoeAxeacCVbcfpX16EhZ+oxNrnd+DZX70Lf0/Q6rKIiIgoyRi80oSuazj2gsig+4837MFff1SPthYOuiciIppMGLzSzMLjqnDGtYvQtbcXj373Tbzzwg6EQ2GryyIiIqIkYPBKQzP2K8J531iC8jn5eOWxD/H4D+qxa1u71WURERHRBDF4pam8EjdO//IinHz5/uja24vHf1CHl//8Pno59ouIiChj8ZTpaUxEMLd2OmbuX4w3ntyCdf/x4KO3W3D0eXOxzyHTIMLLDREREWUS9nhlAKfbhmPPn4dzb65FdoET//rNBjz983fQ1tJtdWlEREQ0BpIJVwavra1VdXV1VpeRFsJhhXUvefDGU1sQDinUnlqNg06aCd3ODE1EmSsYCMHfE4K/J4jeniD8fT++IPw9of55AJBT6ERukQs5hS7kFDmRleuAaDwCQOlDROqVUrXxlvFQY4bRNMGiE2ZgzkHT8MpjH+CNp7bggzeb8IkL90XlvEKry0t7SimEgwoBfwhBfxhBfwjBQGQ6Zl7/dNhYHkKgf1nsOgF/CKFAZN2sfAcKpmehsCwrcjs9G/nT3NBtDMY0OSmlEOgdCE2RoBQdnkLw+yL3A33zB4Upvy+IcHDkTgC7U4cKKwQDsd/01myCnEIXcgudyClyGaHMmDbCmcPFt7upTimFrr296O0Oorgyx7I62OOV4bat242XH/kAHV4f9ju8DEeesw/cuQ6ry0q5vc3d2PJ2C3q7g0Z4ig1FsSEqEp76ghPG8ZLXbRpsDg02h95/a++/r8Nm16DbNHS19aK1qRtde3v7txVNkFfsGghjZdkoKMtC4fQsuHLsHKtHaUEphd7uILrb/Ohu70VXmx/dbX74ugMDvU/9gSoqZPlCUCNdbUMAh8sGh1uH022Do+/HFbl1uvVB9/vW0fvnOdw2aJr019mxx4fOPT507OlFZ2vsdNfeXgx+a3Nm2SLhrCgSyPp7zYzp7AIndJ0fkCaDoD+Evc09aG3qwt5d3Wht6sbeXZGfQG8IheXZuPD2w1JaQ6IeLwavSSDgD6Hu2W1Y+6+PYXfrOPLsfTD/iPJJ1/UeCoWx7Z3d2PDfndixqRVA5JOu3aFDtw8OQ8atPc48hwabPTpA6UNClc2hxexXG2Nb+n3BmD/4yG0X9u7qQSg48GndmW1D4fQsFJRlR26N3rK8UjffBCgpwqEwejoD6G7zo6utd0iwGpjnj3lt9tE0GQhBw4UjY35MqIoKWXaHbur/o3AojK42fySctfrQuad3IKi19qJzjw+93bHfEBcBsguc/YcvIz1lAwEtt8gFZ7aNH5TShFIK3e1+7G3qRuuu7piQ1bHHN/ABW4DcQuODr/GBt6giGxVzU3uEiMFrivA2dOI/f3ofjZvbUL5PPj5x4b4orrCuOzVZOvb4sPGVBmx8pQHd7X7kFDqx/zEVmH9kBbILnFaXNybhsELnHh9ad3VH/mFE/bPobvf3r6dpgrxSd1QvmdFTNj0Lrmy7hc9gZCqs4O8NDe0l8QWhQgrObDvcOQ64cmxw5TjgcOl8MxuHoD9khKdedLf7+6e72v0x4crX4R/S+wNEQn9WnhPZ+Q5k5TuQnedE1pBp56T9/fh9QXQaISwS0Izp1oGes8GHP20ObVCvmQvZ+Q7odg2aLtA041YX6PrAtBYzPcJ9TSZle49XKBDG3pbu/oA1cNsFvy/Uv57NofX/j4z+v5k/LQt2h2563QxeU4gKK2z6XyNee2IzAj0hLD5pJmpPq7bkhTcR4bDCxxu82PDfBmxftxsKwKwDinHAMZWYeUDxmHugMkFvT9D4p9IV01PW1tyNcGjg79SdazfGj0X1lJVlIa/YBW2CvWThUBh+X7wBznHm9QTR2xNCwDdofu/YDudqmsCVY4/8ZNvh7pvOMQJadiSgRe5H5tudkzMMRB/u62o3eqJipgdCVrzruYomyMq1IyvfCFR5joHpfKdxPxKs+IWcxFRYoacz0N9T1tka1Wtm3I/+sJRMmjbKkKZr0HWBaFH3bQK7S4fTZYM9zmHb6F5Jp9sW+Vuy+P+pUgq+zgBaoz+M9vVe7e6J+eCQU+iM/f9nhKycAqflzyMag9cU1NPpx2t/3Yz3/teE3GIXjj1/HqoXllhd1oi62nqx6bVGbPxvAzr2+ODOc2DBUeVYcHQF8ordVpdniXAojHavL+aTXqsxXqGnI9C/nqYL8qfFftpzOG0xA5gHBjiH+gdBRweqoH/ky1NpNon883bF/gMfPB4n3qEoTRP4ugPwdUZ+ejoD8HVF3/dHpo15w/170mwSG9KyHVHT9piQ1nd/omEtHFaDvlyR6IsXA1+6iP+ljeht+sYohhHsDSEcZ7yUzaFFApQRnOIGqzwnXDn2SfmhJF2FAmF0d0QO0aqwQjgU+QmFwv3T4ZjpwffDCA2eHx5+3Xj7CcVZL9Ab6h+LN6rxd049zt9t3zg8fcj8wWPyHC59VB/6QqEw2lt6oj5YDvT4Rx/61e1aVLiK3BaWRb6olClfkmDwmsJ2ftCK//zpfbQ2dWPOQaU4+rx5yClMr8NzKqzg+aAVG17eia1rdyMcVqjarxD7H1OJmsUlHOuUgK8r0P8PLLqXrL2lZ9g38NhwpMf8kx0IVHqceZFps3pKVFihtyfYH8R6OgcCmq/LP+j+QIgbrrdNt2lDetYcWTaEA+FICAoMClVRoSjgD43qW3eDiWDQ2MGoMYf2OGMPHTrcOXZk5xuH+vIcyM53wj5JD/dRaimlEPSHY3urfQNfjhjcg+33hYacyqO3Z3TfOLU5dThdsf837Mb/kp6OyP+pwf+XsvIdxgfFbCNcRT405ha50qr3ajwYvKa4UDCMt5/7GHXPboOmCQ47czYWHlc54cNSE+XrDGDT/xqx4b870dbcA2e2DfOPKMf+x1SiYHqWpbVlur5PlkF/eOCfoFuf9CE2HFbwdwcjPWddQfg6hwloxnRvdwC6feCLFPZBIWnIN1jtcb6A4Yjazq7HLNNsHK9DmS/6HGv+wUMLok4XMtwwBFe2Pab3qm+IhMOdGb1X42Fp8BKRbQA6AIQABJVStSJSBOBRANUAtgE4TynVOtw+GLySo62lGy//+QN8vHEPSmfm4hMX7ovp1Xmm1qCUQtNHbVj/3534qL4FoWAY5XPysf+xlZhzcCls9swai0ZERDRYOgSvWqXU7qh5PwSwRym1QkRuBlColLppuH0weCWPUgqb65vxymMforvdj4WfqMJhy2bDmeJPHr09QXzwRhPWv7wTexq6YHfp2O+wMux/bKWlJ7IjIiJKtnQ8c/0yAMcZ0w8BeAnAsMGLkif+hbebcfS5qbnwdvP2dmx4eSc+eGsXgv4wSmfm4viL9sM+tdMyZpAkERFRspjR47UVQCsiQ17/Tym1UkT2KqUKjOUCoLXvfjzs8Uqd5u3teOmP76Pl4w7M3L8Ix54/D/mlExtfFegN4cO6Xdjw8k40b++AzaFh7pLpOODYSkybZe6hTSIiIrNZfaixUim1U0SmAXgOwJcBPBUdtESkVSlVOGi7KwBcAQAzZ848ZPv27SmtcyqLe+Htk2eO+fqC3p2d2PDfBrz/eiP8vhCKKrKx/zGV2Pew6XBmpfdJP4mIiJIlbb7VKCJ3AOgE8P8AHKeUahSRcgAvKaX2HW479niZo7O1F6889gE+WtOCwrKsUV14OxgI4aM1Ldjw351o3NwGzSbY5+Bp2P/YSpTPyec3uoiIaMqxLHiJSDYATSnVYUw/B+A7AE4E4I0aXF+klPr6cPth8DLXkAtvf3ofuHNiL7y9d1c3NrzSgPdea4SvK4D8Ujf2P6YS+x1ZNmRdIiKiqcTKwfXTAawyej1sAP6klPqniLwF4C8ichmA7QDOS3EdNAbVC0tQuW9h/4W3t67bjSPP3gf7HlaGbe/uxvqXd8LzXitEE8xeVIL9j61E1b6FGX/COyIiolTjCVQpoegLb9vsGoKBcEZfpJqIiCjV0vF0EpQhiitysPwrB+O91xux84O92OfgaZP2ItVERESpxuBFIxJNMP/ISA8XERERjd/kvnAbERERURrJiDFeItKCyCD8qaYEwO4R15ra2EaJsX0SY/uMjG2UGNtnZFOxjWYppUrjLciI4DVViUjdcIPzKIJtlBjbJzG2z8jYRomxfUbGNorFQ41EREREJmHwIiIiIjIJg1d6W2l1ARmAbZQY2ycxts/I2EaJsX1GxjaKwjFeRERERCZhjxcRERGRSRi8iIiIiEzC4EVERERkEgYvIiIiIpMweBERERGZxLKLZIvIKQB+CkAH8Bul1Irh1i0pKVHV1dVmlUZEREQ0bvX19buHu2SQJcFLRHQA9wE4CYAHwFsi8pRSamO89aurq1FXV2dmiURERETjIiLDXl/aqh6vQwFsVkptAQAReQTAMgBxgxdZSykF747t2LX1I4SCQahwCOFQCOFQGGFjWoVCxnS8eZH5A9sZP1H3Vd+2UfMj2yeYFwpB0zRoNhs0TYPoOjRdh6b13caZp2uQ/unIfDHWjcyzQdO12GX922vx5+l6/z7tTifsLhccLjfsLjccLtfArdMF0SbX0f1wOAR/Tw/83d3o7emGv7sb/p5u9HZ3wd/TE5lnzO/t7kbQ3wvRNIhI5EfTABGIaBDNmCdaZB3NmDbWi95GRID+ZaNYL2adqMcQGfpaMH7Pw86L+n3H/BivB4l6vYmI1b8iIkozVgWvSgA7ou57ABxmUS0UR8Dfix3r38WWNW9hy9tvoWN3y6i2E02LCTr9ocZmiwkvA8v0mKBjczr7Q9Ow4afvjU/TEA6HRwiCA0FNhUIIBYMIh3r711WhEEJDQmJkGzUoIKpweMLtane6ooKZEcrcbjickenIMhcc7ixj+cC6Dqcbdndywlw4HIK/uycSkhIEpt7urtj1eiIBqi9MBXp9Iz+YCJzuLDjcWbA5HAAUVFhBqTCUGjwdfRs2lkWWI8426U5EGxLGhv9QEPnRbXbYHHbodgdsdgd0ux02hwM2h6N/ns1uh+4wbu2O/uX969v71o9M647YeZqmW900RFOWZWO8RiIiVwC4AgBmzpxpcTVTQ3tLc3/Q2rH+XQQDftidLsw6cDEOP/t8VM0/AHYjGPUHrOiQ1dfDMEn1BYKhvXVhI9CFEPT3IuDzwe/rgd/Xg4DPh4CvB/7o2x5jWW9knq+zAx27W+Dv6THW6UE4FBp1XTanEw6XOzbMGYFNKZWUwORwu+HIyoIrJxd5pdPhdLuN+VlwZmXDkeXuX9eZlR1ZZsxLZU9fXygbKaANG+rC4UHhfaDnNja8D8wLhYJDel77Qnx40HwVjj8vFAzFfcxwKIhQMIhQwG/0EPoRCgQQDPgRDAQQ8vsR9PsnHDo13RYT7gam7bA5nP1hT7c7oBs9d8P3Tsb2JmJI7+Og9fuWjaV3MmYdGfhdGr9HGNOR36UCBv/OY6YHXi9jWi+sIvtX4cjjRW0z7HPVxtYzq2naoLZO0I7G/hBn3yrOazPU9/obxdGJcCgY+wF0hKMTQ17vg/aNvp7l6A8cui3qSMNIvcvGvOgP8IN7nrU48wa/Rxn7ceXkYs4hhybpv9DYWRW8dgKYEXW/ypjXTym1Esb1nWpra3ldoxQIh0Jo+GATtrxdhy31b8Lr+RgAUDC9HAd+8hTUHLwEVfMPgM1ut7jS9CAi/f80Ui0UDMSGNCPMDQS5QWEuZp0e+Lo60b67BSKSloEpWSJvQvqU+352OBSKhDEjiIWig1nfdMCPoD+AoL93ILxFBbmB5f6B+8Y+/D3d6G5vi4S8UChxkFUKiBNWBqbDRnhJv3/jg8MMRgh9kWk9dhsAalAvLkYIcunQgzvs0Ylhh2HEhiObw9E/zCPx0YlIG41lyEnQ7x/TkJOBn+Conntx1cwpGbzeAjBXRGoQCVznA7jQolqmlO72Nmx7Zw22rHkL299ZA19XJzRdR9X8/XHA8Sdh9sFLUFheOal7rjKBbrPDnWOHOyfX6lIoDWm6Doce6eXMFOPqnRzUo9W3jmj68OFI00YdqNJJXziNF1oHtw1GCHWaNnQs61QZfxjpxY7teYsdNhK2/LlbEryUUkERuQbAakROJ/GAUmqDFbVMdkoptGzf2n8IsfHD9wGlkJVfgDlLDsfsg5dg1sKD4MzKsrpUIprEpmrv5Gj1HUaNRAKOwRsv0TTomgbdlr5Haiwb46WUehbAs1Y9/mQW8Pmwff072GqErc49XgBA2Zy5OOKcCzD74CWYXjMn7Q8jERERTTZpO7iexmbvriZsWfMWtr79FnZsXIdQIACH241ZBx6E2QctQc1BtcguKLS6TCIioimNwStDhYJB7HxvI7a8/Ra2rnkLexo8AIDCiiosPvk0zD54CSr3W5DW3a1ERERTDYNXBulu24uta+uxZc1b2PbOGvh7uqHbbKhasBCLTv4UZh+0BAVl5VaXSURERMNg8EpjKhxG87Yt/QPjmz76EFAKOYVF2PeIo1Fz8BLMWrg4o77ZRERENJUxeKWpNc8+iTeffBxde1sBEZTvMw9HnftZ1By8BNOqZ1v+dVgiIiIaOwavNLTuxX/hxYd+jZkHLMKxn/0Cqhcfgqy8fKvLIiIiogli8Eoz29bW47mVv8CsAw/C8ptuh27jr4iIiGiy4Imc0kjzti146t4VKJkxC2fccAtDFxER0STD4JUm2nc344kVd8CZnY3lN9/OM8kTERFNQgxeacDX1Yknvn8HAj4fzrn5DuQWlVhdEhEREaUAg5fFQsEAnrrne2htbMCyG29Fycxqq0siIiKiFGHwspBSCqvv/xl2bHgXS790LWYesMjqkoiIiCiFGLws9Npf/oBN/30RR513ERYce4LV5RAREVGKMXhZ5N0XVuP1Jx7FwhNOxmFnf8bqcoiIiMgEDF4W2Lq2Hs//5j5ULz4EJ152Fc9CT0RENEUweJls19aP8Pd7V6BkZjXOuP4mnquLiIhoCmHwMlF7SzNWrbgDruwcnH3T7XC4ea4uIiKiqYTByyS+rk48seIOBP1+nH3LHcgpKra6JCIiIjIZg5cJgoEAnrr7LrQ2NuDMr96KkhmzrC6JiIiILMDglWJKKfzr/p9ix8Z1OOXK6zDzgAOtLomIiIgsMqHgJSLnisgGEQmLSO2gZbeIyGYReV9ElkbNP8WYt1lEbp7I42eCVx/9PTa98hKOPv8SzD/meKvLISIiIgtNtMdrPYCzAbwcPVNEFgA4H8D+AE4B8EsR0UVEB3AfgFMBLABwgbHupPTu8//EG6v+goUnLsWhZ51rdTlERERksQmdy0AptQlAvPNQLQPwiFKqF8BWEdkM4FBj2Wal1BZju0eMdTdOpI50tOXtt/D8b3+JmoNq8Umeq4uIiIiQujFelQB2RN33GPOGmz+EiFwhInUiUtfS0pKiMlNj15bNePreH6B0Vg1Ov/4maLpudUlERESUBkbs8RKR5wGUxVl0q1LqyeSXFKGUWglgJQDU1taqVD1OsrU178KqH3wbrtxcLL/pdjhcbqtLIiIiojQxYvBSSn1yHPvdCWBG1P0qYx4SzM94vk7jXF0BPy741l3IKSyyuiQiIqK0EggE4PF44PP5rC5lwlwuF6qqqmC320e9TaquV/MUgD+JyI8BVACYC+BNAAJgrojUIBK4zgdwYYpqMFUwEMCT93wXbbsacc6td6K4aqbVJREREaUdj8eD3NxcVFdXZ/T4Z6UUvF4vPB4PampqRr3dRE8nsVxEPACOAPCMiKw2itkA4C+IDJr/J4CrlVIhpVQQwDUAVgPYBOAvxroZTYXDWP2rn8CzcT2WXnk9ZixYaHVJREREacnn86G4uDijQxcQ+WJhcXHxmHvuJvqtxlUAVg2z7C4Ad8WZ/yyAZyfyuOnmlUcexnuv/gdHX/A5zD/6OKvLISIiSmuZHrr6jOd58Mz1E/TOc8/izScfx4GfPAWHLvu01eUQERFRGkvVGK8p4aP6N/HCb+/H7IOX4MRLr5w0CZ6IiGgy8nq9OPHEEwEATU1N0HUdpaWlAIA333wTDocj5TUweI1T00cf4umf/gDTambjtOu+znN1ERERpbni4mKsXbsWAHDHHXcgJycHN954o6k18FDjOLQ1N2HVD76NrLx8nquLiIhoEnrrrbdw4IEHwufzoaurC/vvvz/Wr18/4f2yx2uMejo78MT370AoGMB5t30f2QWFVpdERESUkV58cCWat29J6j6nzZqN4z9/xYT3s2TJEpx55pn45je/iZ6eHlx00UU44IADJrxfBq8xCAYCeOruu9DW3IRP3/pdFFfNGHkjIiIiyki33XYblixZApfLhZ/97GdJ2SeD1yipcBj//OW98Gxaj9Ou/RqqFkw89RIREU1lyeiZSiWv14vOzk4EAgH4fD5kZ2dPeJ8c4zVK//3zQ3j/tZdxzIWfx35HfcLqcoiIiCjFvvjFL+LOO+/EZz/7Wdx0001J2Sd7vEZh7epn8NZTf8Wikz6FJWeeY3U5RERElGIPP/ww7HY7LrzwQoRCIRx55JH497//jRNOOGFC+xWlVJJKTJ3a2lpVV1dnyWN/VP8GnvzRXag56BAsu/GbPG0EERHRBGzatAnz58+3uoykifd8RKReKVUbb30eakygafMHePqnP8S0mjk4/bqbGLqIiIhoQniocRh7dzVh1Q+/g6y8Aiy/6TbYXS6rSyIiIqIkiz6bfbQXXngBxcXFSX88Bq84ejra8cSKOxAOBnH27TxXFxER0WQVfTZ7M/BQ4yBBvx9P3v1dtDc3YdnXvoniSp6ri4iIKJkyYXz5aIzneTB4RVHhMP7xy3ux872NOOXqr6BqPs/VRURElEwulwterzfjw5dSCl6vF64xDkXiocYoL//pQXzwv//i2IsuxX5HHmt1OURERJNOVVUVPB4PWlparC5lwlwuF6qqqsa0DYOX4e1//h11f38Ci5eehtrTl1tdDhER0aRkt9tRU1NjdRmW4aFGAL6uTrz22J8w+5BDcfznr4CIWF0SERERTULs8QLgys7B+d/5IfKKS6FpPFcXERERpQaDl4HfXiQiIqJUy4hLBolIC4DtVtdhgRIAu60uIs2xjRJj+yTG9hkZ2ygxts/IpmIbzVJKlcZbkBHBa6oSkbrhrvVEEWyjxNg+ibF9RsY2SoztMzK2USwOriciIiIyCYMXERERkUkYvNLbSqsLyABso8TYPomxfUbGNkqM7TMytlEUjvEiIiIiMgl7vIiIiIhMwuBFREREZBIGLyIiIiKTMHgRERERmYTBi4iIiMgkDF5EREREJsmIi2SXlJSo6upqq8sgIiIiGlF9ff3u4a7VmBHBq7q6GnV1dSnbvwortD+/Ha65hXDW5KfscYiIiGjyE5Htwy3joUYAyh9Cz7u74f3jJoTaeq0uh4iIiCYpBi8AmsuG4ovnQ/nD8P5hE1QwbHVJRERENAkxeBns07NRdN48+Hd0YO9TH1ldDhEREU1CGTHGyyzuA0qQe/wMdLy4A/bKHOQcVm51SURERJNKIBCAx+OBz+ezupQJc7lcqKqqgt1uH/U2DF6D5J00C/6dndj71Eewl2XDOSvP6pKIiIgmDY/Hg9zcXFRXV0NErC5n3JRS8Hq98Hg8qKmpGfV2PNQ4iGiC4vP3hZ7vhPcPmxBq91tdEhER0aTh8/lQXFyc0aELAEQExcXFY+65Y/CKQ8uyo/jiBVC+ILx/5GB7IiKiZMr00NVnPM+DwWsYjvJsFH56Hvzb27H36S1Wl0NERESTAMd4JZC1qBT+nZ3ofNkDR2UOspeUWV0SERERjZPX68WJJ54IAGhqaoKu6ygtjZxg/s0334TD4Uh5DQxeI8hfWo1AQyda/7YZ9rJsOGbkWl0SERERjUNxcTHWrl0LALjjjjuQk5ODG2+80dQaGLxGILqg6IL90PyLt+H9/UZM+/JB0HNTn4iJiIgmu71//wj+hq6k7tNRkY2CM+ZMeD+33XYbioqKcP311wMAbr31VkybNg3XXXfdhPbLMV6joGdHBtuHe4zB9iEOticiIprMLr30Ujz88MMAgHA4jEceeQQXXXTRhPfLHq9RclTkoPCcudjzyPtoe2YrCs6ceJomIiKaypLRM5Uq1dXVKC4uxttvv41du3bhoIMOQnFx8YT3O+EeLxGZISIvishGEdkgItcZ8+8QkZ0istb4+VTUNreIyGYReV9Elk60BrNkLZ6GnKMr0flaA7rqd1ldDhEREaXQ5ZdfjgcffBC/+93vcOmllyZln8no8QoC+KpSao2I5AKoF5HnjGX3KqXujl5ZRBYAOB/A/gAqADwvIvOUUqEk1JJy+afWRAbbr9oM+/QsOKo42J6IiGgyWr58OW677TYEAgH86U9/Sso+J9zjpZRqVEqtMaY7AGwCUJlgk2UAHlFK9SqltgLYDODQidZhFtEFRRfuBz3bHjmzfSfPbE9ERDQZORwOHH/88TjvvPOg63pS9pnUwfUiUg3gIABvGLOuEZF3ReQBESk05lUC2BG1mQeJg1ra0XMcKL54PkKdfuz503tQIWV1SURERDQGd9xxx4inkgiHw3j99ddx2WWXJe1xkxa8RCQHwF8BXK+UagfwKwBzACwG0AjgnjHu7woRqRORupaWlmSVmTSOqlwULp+L3i1taPvHVqvLISIioiTauHEj9tlnH5x44omYO3du0vablG81iogdkdD1R6XUEwCglNoVtfzXAJ427u4EMCNq8ypjXgyl1EoAKwGgtrY2LbuUsg+ZjsDOTnS+shOOqhxkLZ5mdUlEREQ0BtFns4/2wgsvYMuW5F8ycMLBSyJXiPwtgE1KqR9HzS9XSjUad5cDWG9MPwXgTyLyY0QG188F8OZE67BK/mk18Dd0ovWvH8I2LQuOihyrSyIiIkprSqm0uVB29Nnsx0qpsfcLJeNQ41EALgZwwqBTR/xQRNaJyLsAjgdwg1HkBgB/AbARwD8BXJ0p32iMR3QNxZ+dD81tg/f3GxHqClhdEhERUdpyuVzwer3jCi3pRCkFr9cLl8s1pu0kE554bW2tqqurs7qMhPw7OtB8/ztw1uSj5AsHQPT0SPJERETpJBAIwOPxwOfzWV3KhLlcLlRVVcFut8fMF5F6pVRtvG145vokcczIReFZ+6D1rx+i7V/bUHBqjdUlERERpR273Y6amqn7HsnglUTZS8rg39mJzv944KjMQdaBpVaXRERERGmEF8lOsoLTZ8MxKw+tj32AQFNyr7hOREREmY3BK8nEFhlsLy4bdj+8EeFuDrYnIiKiCAavFNDzHCi+aD5Cbb3wPvI+VDj9v8BAREREqcfglSLOWXkoOHMOej9oRftz260uh4iIiNIAg1cKZR9ahuwlZeh4cQe61+22uhwiIiKyGINXCokICpbNgWNGLlofex+BXRxsT0RENJUxeKWY2DQUXzQf4tDh/f0mhHuCVpdEREREFmHwMoGe70TxZ+cjuMeHPY9ysD0REdFUxeBlEmdNPgrOmA3fe3vQ/sLHVpdDREREFmDwMlH24eXIOmQ6Ol74GD0bvVaXQ0RERCZj8DKRiKDwrH1gr8rBnkffR6C52+qSiIiIyEQMXiYTu4biixZAbBq8v9+IsI+D7YmIiKYKBi8L2AqcKLpwPwS9Pdjzlw842J6IiGiKYPCyiGtOAfI/NRu+jV50vLjD6nKIiIjIBAxeFso5qgJZB01D+/Pb0fPeHqvLISIiohRj8LKQiKBg+T6wl2djzyPvIbC7x+qSiIiIKIUYvCymOfTIYHtN4H14I8K9HGxPREQ0WTF4pQFbkSsy2L6lG62PfQClONieiIhoMmLwShOufQqRf2oNetZ70fEfj9XlEBERUQoweKWRnGMq4V5UivbV2+B7n4PtiYiIJhsGrzQiIig8Zy7s07Ph/fP7CHo52J6IiGgysVldAMXSHDqKL56PXb9Yi6Z76mArcsNWGvmxl2YZ01nQs+1Wl0pERERjxOCVhmzFbkz74oHofrcFwZYeBJq74fuwFQgODLrXsmywGUGsP5BNy4Kt0AXRxcLqiYiIaDiWBS8ROQXATwHoAH6jlFphVS3pyF6Wjfyy7P77KqwQavUh0NKDYEt3JJC1dMP33h501+0a2FAX2IpdsJVmwW70jvWFM83NnE1ERGQlS96JRUQHcB+AkwB4ALwlIk8ppTZaUU8mEE1gK3bDVuwG9iuKWRbuDiCwuwfB5h4Ed3cj0NyDYHM3fJv2AFHXgdRy7JFANs0NW0kWbNMigUwvcEI09pIRERGlmlVdIIcC2KyU2gIAIvIIgGUAGLzGQcuywznTDufMvJj5KhRGcI8PQaOXLNJb1oPud3dD9USdqNUmsJe4hx66LHVDc7KXLJoKKYR7Agj3BBHuDhq3gf5phBSgFJQCoBQQdasG3Y8/b+i2ylg30Tqx+xpYRwBAE0CTyCFoiYR4aBK51QUQGZhnrBO9XLTIOvHmiYbIPjSBSNQ+tKjt+tbXENlGBqYhkS+VAIg8rkTVGHU/sp3xS5DofcVZp/9+/HViHpPSQv9rNxz1Wg5Hfvpf62H0n+Ow7zU07GuU0krf71OFFBBSUKFwzDRCCioYhgorIGgsj5pGWEHFm47eftA0jMeIN60XOFF84XzL2sOqd9VKANFXhvYAOCx6BRG5AsAVADBz5kzzKptERNdgL82CvTQLQHH/fKUUwl0BBI1eskDfocuGTvSs3x35B2jQ8xyRsWMlbtiKXBCnDnHo0OwaxK5BHPrArUOD2HVoDg2waWn95qYC4UiA6u4LUHHCVJxwpXpDiXesR72xCxC5g4GQkjAoDNo2wTrxQkbcQNMXykIKKhD5pxVWxj+88KA3t6h5Mcv6pifbeX372hhp9jqN/n2O5jWEqPta/NdQZJXYdWJeQ0DsvOjgGxWKVDhqesj8SDjq/2DQ/9qKXg/GetGBKsmvregPDv0fBhDz4WDgw4AxL+bDwqBA1/chY9A6Ax9UZCAY9rWDippO8Lyj58cGzqjpYedHPU5fu6fl36lRXyrpAtE1iK3v960BtsjvKnoaugZx6tBc1nYopG13hlJqJYCVAFBbW5t2L6VMJiLQcxzQcxxwVufHLFPBMILenv4xZMHmHgR296D77eaRQ0fMgyASyOzxg9mQ0GbcaoPWjV6uRc93aIAmUP44Aao/LMUGKBW1TAUS/CfQAM1th5Zlg+a2Qc91wD4tq/++lmWPnXbboGXZIC7bpP60rfreWMOJA9rALeIvS9h7F29e/B69eD2G4+pVTDejaoMxPL8x9Zoab+RRPbfRAX8giABaX7CJCToYCDOD50vsOpH1MBCORprf98ECGMXrEAlfm/Fep0OWBcMIx8wbZr2+RuoPxkPrjp4f+/yMNrVF/p9pw7THwAerob+H/vlaX7juC+xpxAipohvhR5eBsDTMdMLlRnuJrg087wxiVfDaCWBG1P0qYx5ZTGwa7NOzYZ+eDXfUfKUUlC8E5Q8hHAhD+UNQfbf+MFQgcj8cb/6g5aGuQGRe9LqJgtCwxSLxG6dNYoKRXuSGvcpmhKaB+UMClFPPuD9kM0R6BhD5x2d1MUREGcqq4PUWgLkiUoNI4DofwIUW1UKjICIQtw1w26CnYP9KGZ8wo8PaoGAWHhzogmFozujwNChA2VNRKRER0fhZEryUUkERuQbAakQ+Qz+glNpgRS2UHkQEsOvQ7ToAnhyWiIgmJ+n7lkg6E5EWANutrsMCJQB2W11EmmMbJcb2SYztMzK2UWJsn5FNxTaapZQqjbcgI4LXVCUidUqpWqvrSGdso8TYPomxfUbGNkqM7TMytlEsXiSbiIiIyCQMXkREREQmYfBKbyutLiADsI0SY/skxvYZGdsoMbbPyNhGUTjGi4iIiMgk7PEiIiIiMgmDFxEREZFJGLyIiIiITMLgRURERGQSBi8iIiIikzB4EREREZnEkotkj1VJSYmqrq62ugwiIiKiEdXX1+8e7lqNGRG8qqurUVdXl9LH8Hq9cLvdyMrKSunjEBER0eQmItuHW8ZDjQB8Ph9WrlyJZ555xupSiIiIaBJj8ALgcrlw1FFHYcOGDVi3bp3V5RAREdEkxeBlOOqoo1BVVYVnnnkG7e3tVpdDREREk1BGjPEyg67rWL58Oe6//348+eSTuOiiiyAiVpdFREQ0qQQCAXg8Hvh8PqtLmTCXy4WqqirY7fZRb5PS4CUiDwA4HUCzUuoAY14RgEcBVAPYBuA8pVRrKusYreLiYpx88sl45plnUFdXhyVLllhdEhER0aTi8XiQm5uL6urqjO7gUErB6/XC4/GgpqZm1Nul+lDjgwBOGTTvZgAvKKXmAnjBuJ82amtrMWfOHPzrX/+C1+u1uhwiIqJJxefzobi4OKNDFwCICIqLi8fcc5fS4KWUehnAnkGzlwF4yJh+CMBZqaxhrEQEy5Ytg67rWLVqFUKhkNUlERERTSqZHrr6jOd5WDG4frpSqtGYbgIw3YIaEsrLy8Npp50Gj8eDV1991epyiIiIaJKwdHC9UkqJiIq3TESuAHAFAMycOdPUugBg4cKFeO+99/DSSy9h7ty5KC8vN70GIiIiSi6v14sTTzwRANDU1ARd11FaGjnJ/JtvvgmHw5HSx7eix2uXiJQDgHHbHG8lpdRKpVStUqq2r0HMdtpppyErKwtPPPEEAoGAJTUQERFR8hQXF2Pt2rVYu3YtvvSlL+GGG27ov5/q0AVY0+P1FIDPAVhh3D5pQQ2jkpWVhWXLluGPf/wj/v3vf2Pp0qVWl0RERDRp/OMf/0BTU1NS91lWVoZTTz01Kfu6//77cf/99wMA2traUF1djRdffHFC+0xpj5eI/BnA/wDsKyIeEbkMkcB1koh8COCTxv20NXfuXNTW1uJ///sftm3bZnU5REREZJIvfelLWLt2Ld566y1UVVXhK1/5yoT3mdIeL6XUBcMsOjGVj5tsJ598MrZs2YJVq1bhyiuvhMvlsrokIiKijJesnqlUu+6663DCCSfgjDPOmPC+eMmgUXA4HFi+fDna29uxevVqq8shIiIikzz44IPYvn07br/99qTsj8FrlGbMmIGjjz4ab7/9Nt577z2ryyEiIqIUq6+vx913340//OEP0LTkRCYGrzH4xCc+gbKyMjz11FPo7Oy0uhwiIiJKoV/84hfYs2cPjj/+eCxevBiXX375hPcpSsU9jVZaqa2tVXV1dVaXAQDYtWsXVq5ciblz5+Izn/nMpDn7LhERkRk2bdqE+fPnW11G0sR7PiJSr5Sqjbc+e7zGaPr06TjhhBPw3nvv4Z133rG6HCIiIsoglp65PlMdccQR+OCDD/Dss8+iuroaBQUFVpdERERE4xR9NvtoL7zwAoqLi5P6WAxe46BpGs466yz86le/wt/+9jdccsklSRt0R0RERObqO5u9GZgWxqmwsBCnnHIKtm3bhjfeeMPqcoiIiDJGJowvH43xPA8Grwk46KCDMG/ePDz//PNobo57yUkiIiKK4nK54PV6Mz58KaXg9XrHfFJ1fqtxgjo7O/HLX/4S+fn5uPzyy6HrutUlERERpa1AIACPxwOfz2d1KRPmcrlQVVUFu90eMz/Rtxo5xmuCcnJycMYZZ+DRRx/Ff/7zH5xwwglWl0RERJS27HY7ampqrC7DMjzUmATz58/HokWL8N///hcej8fqcoiIiChNMXglyamnnorc3FysWrUKfr/f6nKIiIgoDTF4JYnL5cJZZ50Fr9eL559/3upyiIiIKA0xeCXR7Nmzcfjhh+PNN9/ERx99ZHU5RERElGYYvJLsxBNPRElJCf72t7+hp6fH6nKIiIgojTB4JZndbsfZZ5+Nrq4uPPvss1aXQ0RERGmEwSsFKioqcOyxx2LdunXYsGGD1eUQERFRmmDwSpFjjjkGFRUVePrpp9HR0WF1OURERJQGGLxSRNd1nH322QgEAnjyyScz/tIIRERENHEMXilUUlKCk046CZs3b0Z9fb3V5RAREZHFGLxSbMmSJZg9ezZWr16NPXv2WF0OERERWYjBK8U0TcOyZcugaRpWrVqFcDhsdUlERERkEQYvE+Tn5+O0007Djh078Oqrr1pdDhEREVmEwcskCxcuxIIFC/Diiy+iqanJ6nKIiIjIAgxeJhERnHbaaXC73XjiiScQDAatLomIiIhMxuBlouzsbCxbtgzNzc148cUXrS6HiIiITMbgZbJ58+bh4IMPxquvvort27dbXQ4RERGZyGZ1AVPR0qVLsXXrVqxatQpXXnklnE6n1SURpURHRwcaGhqwc+dONDQ0oKOjAzk5OcjLy0Nubm7/T9/97OxsaBo/D5olHA6jp6cH7e3t6Ojo6P+Jvq9pWszvaPDvzOVyQUSsfipEGcOy4CUipwD4KQAdwG+UUiusqsVsTqcTZ511Fn73u99h9erVOPPMM60uiWjCenp60NDQEBO02tvbAUTGOJaWlqKgoACdnZ1obm5GZ2fnkCs6iMiQYBbvDd/pdPLNfgS9vb1xg9Tg+/FOcZOdnd3f3uFwGK2trfj444/R09MzZF2bzZYwmPX92O12M542UdqzJHiJiA7gPgAnAfAAeEtEnlJKbbSiHivMmjULRx11FF599VXsu+++2Hfffa0uiWjU/H4/mpqa+gPWzp07Y04QXFRUhJkzZ6KyshIVFRUoLy+Hw+GI2UcoFEJXV1fcMNDe3g6v14tt27bB5/MNeXy73Z4wmPVN22yTr1M/FAqhs7MzYZjq6OhAb2/vkG0dDkd/G82aNStu++Xk5AzbboFAIOFj7ty5Ex0dHXG/POR2u0f8nbHHk6YCseIagiJyBIA7lFJLjfu3AIBS6vvx1q+trVV1dXUmVmiOYDCIX//61+js7MRVV12F7Oxsq0saVjgcRnd3t9VlpD2XyzXp3uxDoRCam5tjQlZzc3N/b1Vubi4qKipQWVnZH7TcbnfSHt/v98e8uQ/XgxMKhYZs63a7EwaznJyctHqjV0r1h9HhglVXV9eQ7foOB44UbMwY1qCUgs/nGzEYjrbHc/Dzcbvd7O0cgdvthq7rVpeRlvx+P3p7e5Gbm5vSxxGReqVUbbxlVr1DVALYEXXfA+Awi2qxjM1mw/Lly7Fy5Uo8/fTTOO+889LiH4pSCm1tbf1vsjt37kRjY2PcT9A0VFZW1og9MVlZWWn1ht8nHA7D6/XGHC5samrq78FwuVyorKzEvHnz+kNWXl5eSmtyOBwoLi5GcXHxsOsopdDT0zNs71lHRweamprQ2dmZ0lpTISsrq/+1U1FREfe1lU6vJxGB2+2G2+3G9OnTh10vUY9nR0dHwh5PGllOTs6IQXwyhdjo11Oi0O/z+TBt2jRcddVVltWath/NReQKAFcAwMyZMy2uJnXKyspwwgkn4Pnnn8e7776LRYsWmV5DV1dXTG9GQ0ND/6dqTdMwffp0LFy4EKWlpWnzzz0dKaXQ3d0d8wcf3ZbR0qGHoi9gR//uowO23W5HeXk5lixZ0t+jVVhYmJb/qEUEWVlZyMrKGvHNvrOzs//309XVNaTXxWrRwT3RYb9Mp+s68vLyRgzufr8/5tAqg1hifUcn+tqrra0NO3bsSDg+b7j/RX3TVo7Pm2gPat9zKS4uRnV1NfLy8lBYWGjRszHq4qFG64XDYfzud79Dc3MzrrrqKuTn56fssXw+HxobG2PebNva2vqXl5aW9r/JVlRUoKysbNL+4zdLssbkJDpcNprfUWdnZ0y43rlzZ//h476AHX24kEGbaPIIBAL9HzoS/S8KBAJDtnW5XKP6PzTW/xcjjRnsu5+JYwYTHWq0KnjZAHwA4EQAOwG8BeBCpdSGeOtP9uAFAHv27MGvfvUrVFVV4eKLL07KiyUQCGDXrl39hwsbGhqwe/fu/uUFBQX9b7KVlZUoLy/nqS0slKxvoUX/4+n7piEDNhGNpK93aaQwlGh8XrwwFAwG4+5vMn9LNu2CFwCIyKcA/ASR00k8oJS6a7h1p0LwAoC6ujo8/fTTOPXUU3HYYWMb8hYKhdDS0hLTo7Fr167+N+ns7OyY3oyKioq0HsxP8fUdRhjpH2P0FyEYsIkomcLhMLq6ukbsxY8OVoOD2XDharKcFy4dB9dDKfUsgGetevx0dMghh+D999/Hc889h9mzZ6O0tDTuekop7NmzJ+aQUVNTU38XsdPpREVFBY488sj+N9u8vLxJ8WKe6jRNQ05ODnJyclBeXj7sesFgEJ2dnbDb7QzYRJRU0WNUE+k7lGiz2ZCdnc1vWhos6/Eai6nS4wVEzvT9y1/+EoWFhbjsssug6zra29tjDhc2NDT0DzC12WwoLy+POWxUVFTEsTlEREQWScseL4ovNzcXp59+Oh577DH85je/6f+2BhDpqp0+fToWLFjQf9iwtLSUnyKIiIgyBINXGtp///2xbds2bN26FbNnz+7vzSorK0v7AYVEREQ0PAavNHXaaadZXQIRERElGQcCEREREZkkIwbXi0gLgO1W12GBEgC7R1xramMbJcb2SYztMzK2UWJsn5FNxTaapZSKe2qCjAheU5WI1A33rQiKYBslxvZJjO0zMrZRYmyfkbGNYvFQIxEREZFJGLyIiIiITMLgld5WWl1ABmAbJcb2SYztMzK2UWJsn5GxjaJwjBcRERGRSdjjRURERGQSBi+LiMgMEXlRRDaKyAYRuc6Yv1hEXheRtSJSJyKHRm1znDF/g4j8x7rqzTHWNhKRfBH5u4i8Y6z/BWufQWolaJ9FIvI/EVlntEde1Da3iMhmEXlfRJZaV705xtpGInKSiNQb8+tF5ARrn0Fqjec1ZCyfKSKdInKjNZWbZ5x/ZwcayzYYy13WPYPUGsffmF1EHjLmbxKRW6x9BhZQSvHHgh8A5QAONqZzAXwAYAGAfwE41Zj/KQAvGdMFADYCmGncn2b1c0jDNvoGgB8Y06UA9gBwWP08LGiftwB8wph/KYA7jekFAN4B4ARQA+AjALrVzyPN2uggABXG9AEAdlr9HNKpfaK2exzAYwButPo5pFsbIXJFmHcBLDLuF0/mv7NxtM+FAB4xprMAbANQbfXzMPOHPV4WUUo1KqXWGNMdADYBqASgAPR9csoH0GBMXwjgCaXUx8Y2zeZWbL5xtJECkCsiAiAHkeAVNLVoEyVon3kAXjZWew7AOcb0MkT+4fUqpbYC2AzgUExiY20jpdTbSqm+19MGAG4RcZpbtXnG8RqCiJwFYCsi7TPpjaONTgbwrlLqHWMbr1IqZG7V5hlH+ygA2SJiA+AG4AfQbmrRFmPwSgMiUo3IJ+03AFwP4EcisgPA3QD6umHnASgUkZeMQyCXWFGrVUbZRr8AMB+RILYOwHVKqbDpxVpgUPtsQCRkAcC5AGYY05UAdkRt5jHmTQmjbKNo5wBYo5TqNaVAi42mfUQkB8BNAL5tQYmWG+VraB4AJSKrRWSNiHzd9EItMsr2eRxAF4BGAB8DuFsptcfcSq3F4GUx4x/ZXwFcr5RqB3AlgBuUUjMA3ADgt8aqNgCHADgNwFIA3xKReRaUbLoxtNFSAGsBVABYDOAXg8emTEZx2udSAFeJSD0iXf9+K+tLB2NtIxHZH8APAHzR7FqtMIb2uQPAvUqpTksKtdAY2sgG4GgAnzVul4vIiRaUbKoxtM+hAEKI/J+uAfBVEZltQcnWsfpY51T+AWAHsBrAV6LmtWHgNB8CoN2YvhnAt6PW+y2Ac61+DmnWRs8AOCZqvX8DONTq52B2+wxaPg/Am8b0LQBuiVq2GsARVj+HdGoj434VIuNUjrK69nRrHwD/RWRMzjYAexE5nH+N1c8hzdrofAAPRS37FoCvWf0c0qh97gNwcdSyBwCcZ/VzMPOHPV4WMcYh/RbAJqXUj6MWNQD4hDF9AoAPjeknARwtIjYRyQJwGCLH0ietcbTRxwBONLadDmBfAFvMqdZ8w7WPiEwzbjUA3wRwv7HoKQDni4hTRGoAzAXwprlVm2usbSQiBYgE+JuVUq+aXrDJxto+SqljlFLVSqlqAD8B8D2l1C/MrttM4/g7Ww1goYhkGeOYPoHIF6MmpXG0z8eI/N+GiGQDOBzAe2bWbDWeQNUiInI0Ip8e1wHoG4f0DUQGGf4Uke5qH4CrlFL1xjZfA/AFY/3fKKV+YnLZphprG4lIBYAHEfmWjQBYoZT6g9l1myVB+8wFcLVx/wlEermUsc2tiBwCCCJySOAfphZtsrG2kYh8E5GewQ+jdnOymqRfZhnPayhq2zsAdCql7janWmuM8+/sIkReRwrAs0qpSTvOaxx/YzkAfofINx8FwO+UUj8yt2prMXgRERERmYSHGomIiIhMwuBFREREZBIGLyIiIiKTMHgRERERmYTBi4iIiMgkDF5EREREJmHwIiIiIjIJgxcRERGRSf4/6bbAoNcZh4sAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"You can already see some differences by eye - but for successful machine learning we have to put these differences into numbers.\n\nFor this, tsfresh comes into place. It allows us to automatically extract over 1200 features from those six different time series for each robot.\n\nFor extracting all features, we do:","metadata":{}},{"cell_type":"code","source":"from tsfresh import extract_features\nextracted_features = extract_features(timeseries, column_id=\"id\", column_sort=\"time\")  # 指定‘id’列和 时序排序列","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:05.442676Z","iopub.execute_input":"2021-09-03T08:01:05.443218Z","iopub.status.idle":"2021-09-03T08:01:24.226073Z","shell.execute_reply.started":"2021-09-03T08:01:05.443176Z","shell.execute_reply":"2021-09-03T08:01:24.225016Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stderr","text":"Feature Extraction: 100%|██████████| 10/10 [00:17<00:00,  1.75s/it]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"You end up with a DataFrame extracted_features with all more than 1200 different extracted features. We will now remove all `NaN` values (that were created by feature calculators, than can not be used on the given data, e.g. because it has too low statistics) and select only the relevant features next:","metadata":{}},{"cell_type":"code","source":"from tsfresh import select_features\nfrom tsfresh.utilities.dataframe_functions import impute\n\nimpute(extracted_features)\nfeatures_filtered = select_features(extracted_features, y)","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:24.227601Z","iopub.execute_input":"2021-09-03T08:01:24.228250Z","iopub.status.idle":"2021-09-03T08:01:31.039029Z","shell.execute_reply.started":"2021-09-03T08:01:24.228213Z","shell.execute_reply":"2021-09-03T08:01:31.037742Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/tsfresh/utilities/dataframe_functions.py:172: RuntimeWarning: The columns ['F_x__partial_autocorrelation__lag_7'\n 'F_x__partial_autocorrelation__lag_8'\n 'F_x__partial_autocorrelation__lag_9' ...\n 'T_z__matrix_profile__feature_\"median\"__threshold_0.98'\n 'T_z__matrix_profile__feature_\"25\"__threshold_0.98'\n 'T_z__matrix_profile__feature_\"75\"__threshold_0.98'] did not have any finite values. Filling with zeros.\n  df.iloc[:, np.where(is_col_non_finite)[0]].columns.values), RuntimeWarning)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Only around 300 features were classified as relevant enough.\n\nFurther, you can even perform the extraction, imputing and filtering at the same time with the `tsfresh.extract_relevant_features()` function:","metadata":{}},{"cell_type":"code","source":"from tsfresh import extract_relevant_features\n\nfeatures_filtered_direct = extract_relevant_features(timeseries, y,\n                                                     column_id='id', column_sort='time')","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:31.040323Z","iopub.execute_input":"2021-09-03T08:01:31.040636Z","iopub.status.idle":"2021-09-03T08:01:56.853367Z","shell.execute_reply.started":"2021-09-03T08:01:31.040605Z","shell.execute_reply":"2021-09-03T08:01:56.852181Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stderr","text":"Feature Extraction: 100%|██████████| 10/10 [00:17<00:00,  1.78s/it]\n","output_type":"stream"}]},{"cell_type":"code","source":"features_filtered_direct","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:56.855163Z","iopub.execute_input":"2021-09-03T08:01:56.855645Z","iopub.status.idle":"2021-09-03T08:01:56.907084Z","shell.execute_reply.started":"2021-09-03T08:01:56.855592Z","shell.execute_reply":"2021-09-03T08:01:56.906166Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"    F_x__value_count__value_-1  F_x__abs_energy  F_x__root_mean_square  \\\n1                         14.0             14.0               0.966092   \n2                          7.0             25.0               1.290994   \n3                         11.0             12.0               0.894427   \n4                          5.0             16.0               1.032796   \n5                          9.0             17.0               1.064581   \n..                         ...              ...                    ...   \n84                         0.0          96833.0              80.346334   \n85                         0.0           1683.0              10.592450   \n86                         0.0          83497.0              74.608757   \n87                         0.0        1405437.0             306.097697   \n88                         0.0           1427.0               9.753632   \n\n    F_x__range_count__max_1__min_-1  F_y__abs_energy  F_y__root_mean_square  \\\n1                              15.0             13.0               0.930949   \n2                              13.0             76.0               2.250926   \n3                              14.0             40.0               1.632993   \n4                              10.0             60.0               2.000000   \n5                              13.0             46.0               1.751190   \n..                              ...              ...                    ...   \n84                              0.0          42780.0              53.404120   \n85                              0.0           1523.0              10.076375   \n86                              0.0          21064.0              37.473546   \n87                              0.0         308658.0             143.447551   \n88                              0.0            113.0               2.744692   \n\n    T_y__standard_deviation  T_y__variance  \\\n1                  0.471405       0.222222   \n2                  2.054805       4.222222   \n3                  1.768867       3.128889   \n4                  2.669998       7.128889   \n5                  2.039608       4.160000   \n..                      ...            ...   \n84                39.541483    1563.528889   \n85                 3.841296      14.755556   \n86                52.807154    2788.595556   \n87                80.098162    6415.715556   \n88                 2.628054       6.906667   \n\n    F_x__fft_coefficient__attr_\"abs\"__coeff_1  \\\n1                                    1.000000   \n2                                    0.624118   \n3                                    2.203858   \n4                                    0.844394   \n5                                    2.730599   \n..                                        ...   \n84                                 359.248162   \n85                                  36.770027   \n86                                 312.044052   \n87                                 481.046930   \n88                                  15.524355   \n\n    T_y__fft_coefficient__attr_\"abs\"__coeff_1  ...  \\\n1                                    1.165352  ...   \n2                                    6.020261  ...   \n3                                    8.235442  ...   \n4                                   12.067855  ...   \n5                                    6.445330  ...   \n..                                        ...  ...   \n84                                 309.190088  ...   \n85                                  26.631007  ...   \n86                                 429.697740  ...   \n87                                 683.196535  ...   \n88                                  16.768541  ...   \n\n    T_x__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0  \\\n1                                            0.000000                \n2                                            0.000000                \n3                                            0.000000                \n4                                            0.000000                \n5                                            0.000000                \n..                                                ...                \n84                                          64.000000                \n85                                           4.666667                \n86                                           0.250000                \n87                                           0.000000                \n88                                           4.666667                \n\n    F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8  \\\n1                                                 0.0                 \n2                                                 1.0                 \n3                                                 3.0                 \n4                                                 0.0                 \n5                                                 0.0                 \n..                                                ...                 \n84                                               46.0                 \n85                                                4.5                 \n86                                                7.0                 \n87                                               90.5                 \n88                                                0.0                 \n\n    T_x__quantile__q_0.1  F_y__has_duplicate_max  \\\n1                   -3.0                     1.0   \n2                   -9.2                     1.0   \n3                   -6.6                     0.0   \n4                   -9.0                     0.0   \n5                   -9.6                     0.0   \n..                   ...                     ...   \n84                 203.2                     0.0   \n85                 -41.6                     0.0   \n86                 -84.8                     0.0   \n87                -139.2                     0.0   \n88                 -24.6                     0.0   \n\n    F_y__cwt_coefficients__coeff_13__w_2__widths_(2, 5, 10, 20)  \\\n1                                           -0.310265             \n2                                           -0.202951             \n3                                            0.539121             \n4                                           -2.641390             \n5                                            0.591927             \n..                                                ...             \n84                                          38.559593             \n85                                          14.429645             \n86                                          60.760842             \n87                                         109.029954             \n88                                           2.455593             \n\n    F_y__cwt_coefficients__coeff_14__w_5__widths_(2, 5, 10, 20)  \\\n1                                           -0.751682             \n2                                            0.057818             \n3                                            0.912474             \n4                                           -0.609735             \n5                                            0.072771             \n..                                                ...             \n84                                          71.641254             \n85                                          16.349699             \n86                                          71.095480             \n87                                         173.699573             \n88                                           3.226801             \n\n    T_y__quantile__q_0.1  F_z__time_reversal_asymmetry_statistic__lag_1  \\\n1                   -1.0                                  -5.960000e+02   \n2                   -3.6                                  -6.803846e+02   \n3                   -4.0                                  -6.170000e+02   \n4                   -4.6                                   3.426308e+03   \n5                   -5.0                                  -2.609000e+03   \n..                   ...                                            ...   \n84                  36.4                                  -7.700628e+07   \n85                   1.0                                  -1.050785e+04   \n86                  19.6                                  -5.544922e+06   \n87                 272.6                                  -9.881845e+07   \n88                 -26.2                                  -1.340477e+04   \n\n    F_x__quantile__q_0.2  F_y__quantile__q_0.7  \n1                   -1.0                  -1.0  \n2                   -1.0                  -1.0  \n3                   -1.0                   0.0  \n4                   -1.0                   1.0  \n5                   -1.0                   0.8  \n..                   ...                   ...  \n84                -105.0                  66.8  \n85                   5.8                  10.6  \n86                  30.4                  38.4  \n87                 246.8                 154.8  \n88                 -11.2                   3.0  \n\n[88 rows x 676 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>F_x__value_count__value_-1</th>\n      <th>F_x__abs_energy</th>\n      <th>F_x__root_mean_square</th>\n      <th>F_x__range_count__max_1__min_-1</th>\n      <th>F_y__abs_energy</th>\n      <th>F_y__root_mean_square</th>\n      <th>T_y__standard_deviation</th>\n      <th>T_y__variance</th>\n      <th>F_x__fft_coefficient__attr_\"abs\"__coeff_1</th>\n      <th>T_y__fft_coefficient__attr_\"abs\"__coeff_1</th>\n      <th>...</th>\n      <th>T_x__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0</th>\n      <th>F_z__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8</th>\n      <th>T_x__quantile__q_0.1</th>\n      <th>F_y__has_duplicate_max</th>\n      <th>F_y__cwt_coefficients__coeff_13__w_2__widths_(2, 5, 10, 20)</th>\n      <th>F_y__cwt_coefficients__coeff_14__w_5__widths_(2, 5, 10, 20)</th>\n      <th>T_y__quantile__q_0.1</th>\n      <th>F_z__time_reversal_asymmetry_statistic__lag_1</th>\n      <th>F_x__quantile__q_0.2</th>\n      <th>F_y__quantile__q_0.7</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>14.0</td>\n      <td>14.0</td>\n      <td>0.966092</td>\n      <td>15.0</td>\n      <td>13.0</td>\n      <td>0.930949</td>\n      <td>0.471405</td>\n      <td>0.222222</td>\n      <td>1.000000</td>\n      <td>1.165352</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>-3.0</td>\n      <td>1.0</td>\n      <td>-0.310265</td>\n      <td>-0.751682</td>\n      <td>-1.0</td>\n      <td>-5.960000e+02</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7.0</td>\n      <td>25.0</td>\n      <td>1.290994</td>\n      <td>13.0</td>\n      <td>76.0</td>\n      <td>2.250926</td>\n      <td>2.054805</td>\n      <td>4.222222</td>\n      <td>0.624118</td>\n      <td>6.020261</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>-9.2</td>\n      <td>1.0</td>\n      <td>-0.202951</td>\n      <td>0.057818</td>\n      <td>-3.6</td>\n      <td>-6.803846e+02</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11.0</td>\n      <td>12.0</td>\n      <td>0.894427</td>\n      <td>14.0</td>\n      <td>40.0</td>\n      <td>1.632993</td>\n      <td>1.768867</td>\n      <td>3.128889</td>\n      <td>2.203858</td>\n      <td>8.235442</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>3.0</td>\n      <td>-6.6</td>\n      <td>0.0</td>\n      <td>0.539121</td>\n      <td>0.912474</td>\n      <td>-4.0</td>\n      <td>-6.170000e+02</td>\n      <td>-1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>16.0</td>\n      <td>1.032796</td>\n      <td>10.0</td>\n      <td>60.0</td>\n      <td>2.000000</td>\n      <td>2.669998</td>\n      <td>7.128889</td>\n      <td>0.844394</td>\n      <td>12.067855</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>-9.0</td>\n      <td>0.0</td>\n      <td>-2.641390</td>\n      <td>-0.609735</td>\n      <td>-4.6</td>\n      <td>3.426308e+03</td>\n      <td>-1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>9.0</td>\n      <td>17.0</td>\n      <td>1.064581</td>\n      <td>13.0</td>\n      <td>46.0</td>\n      <td>1.751190</td>\n      <td>2.039608</td>\n      <td>4.160000</td>\n      <td>2.730599</td>\n      <td>6.445330</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>-9.6</td>\n      <td>0.0</td>\n      <td>0.591927</td>\n      <td>0.072771</td>\n      <td>-5.0</td>\n      <td>-2.609000e+03</td>\n      <td>-1.0</td>\n      <td>0.8</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>0.0</td>\n      <td>96833.0</td>\n      <td>80.346334</td>\n      <td>0.0</td>\n      <td>42780.0</td>\n      <td>53.404120</td>\n      <td>39.541483</td>\n      <td>1563.528889</td>\n      <td>359.248162</td>\n      <td>309.190088</td>\n      <td>...</td>\n      <td>64.000000</td>\n      <td>46.0</td>\n      <td>203.2</td>\n      <td>0.0</td>\n      <td>38.559593</td>\n      <td>71.641254</td>\n      <td>36.4</td>\n      <td>-7.700628e+07</td>\n      <td>-105.0</td>\n      <td>66.8</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>0.0</td>\n      <td>1683.0</td>\n      <td>10.592450</td>\n      <td>0.0</td>\n      <td>1523.0</td>\n      <td>10.076375</td>\n      <td>3.841296</td>\n      <td>14.755556</td>\n      <td>36.770027</td>\n      <td>26.631007</td>\n      <td>...</td>\n      <td>4.666667</td>\n      <td>4.5</td>\n      <td>-41.6</td>\n      <td>0.0</td>\n      <td>14.429645</td>\n      <td>16.349699</td>\n      <td>1.0</td>\n      <td>-1.050785e+04</td>\n      <td>5.8</td>\n      <td>10.6</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>0.0</td>\n      <td>83497.0</td>\n      <td>74.608757</td>\n      <td>0.0</td>\n      <td>21064.0</td>\n      <td>37.473546</td>\n      <td>52.807154</td>\n      <td>2788.595556</td>\n      <td>312.044052</td>\n      <td>429.697740</td>\n      <td>...</td>\n      <td>0.250000</td>\n      <td>7.0</td>\n      <td>-84.8</td>\n      <td>0.0</td>\n      <td>60.760842</td>\n      <td>71.095480</td>\n      <td>19.6</td>\n      <td>-5.544922e+06</td>\n      <td>30.4</td>\n      <td>38.4</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>0.0</td>\n      <td>1405437.0</td>\n      <td>306.097697</td>\n      <td>0.0</td>\n      <td>308658.0</td>\n      <td>143.447551</td>\n      <td>80.098162</td>\n      <td>6415.715556</td>\n      <td>481.046930</td>\n      <td>683.196535</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>90.5</td>\n      <td>-139.2</td>\n      <td>0.0</td>\n      <td>109.029954</td>\n      <td>173.699573</td>\n      <td>272.6</td>\n      <td>-9.881845e+07</td>\n      <td>246.8</td>\n      <td>154.8</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>0.0</td>\n      <td>1427.0</td>\n      <td>9.753632</td>\n      <td>0.0</td>\n      <td>113.0</td>\n      <td>2.744692</td>\n      <td>2.628054</td>\n      <td>6.906667</td>\n      <td>15.524355</td>\n      <td>16.768541</td>\n      <td>...</td>\n      <td>4.666667</td>\n      <td>0.0</td>\n      <td>-24.6</td>\n      <td>0.0</td>\n      <td>2.455593</td>\n      <td>3.226801</td>\n      <td>-26.2</td>\n      <td>-1.340477e+04</td>\n      <td>-11.2</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>88 rows × 676 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## [3.pyts](https://pyts.readthedocs.io/en/stable/)\n[参考链接](https://pyts.readthedocs.io/en/stable/auto_examples/index.html#) ","metadata":{}},{"cell_type":"code","source":"!pip install pyts\n\nfrom pyts.classification import *\nfrom pyts.datasets import load_gunpoint\nX_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)\nprint(X_train.shape, y_train.shape)\n\nclf = BOSSVS(window_size=28)\nclf.fit(X_train, y_train)\nprint(clf.score(X_test, y_test))\n\nclf = SAXVSM()\nclf.fit(X_train, y_train)\nprint(clf.score(X_test, y_test))","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:01:56.908392Z","iopub.execute_input":"2021-09-03T08:01:56.908717Z","iopub.status.idle":"2021-09-03T08:02:05.026278Z","shell.execute_reply.started":"2021-09-03T08:01:56.908688Z","shell.execute_reply":"2021-09-03T08:02:05.025034Z"},"trusted":true},"execution_count":37,"outputs":[{"name":"stdout","text":"Requirement already satisfied: pyts in /opt/conda/lib/python3.7/site-packages (0.11.0)\nRequirement already satisfied: joblib>=0.12 in /opt/conda/lib/python3.7/site-packages (from pyts) (1.0.1)\nRequirement already satisfied: scikit-learn>=0.22.1 in /opt/conda/lib/python3.7/site-packages (from pyts) (0.23.2)\nRequirement already satisfied: numpy>=1.17.5 in /opt/conda/lib/python3.7/site-packages (from pyts) (1.19.5)\nRequirement already satisfied: scipy>=1.3.0 in /opt/conda/lib/python3.7/site-packages (from pyts) (1.6.3)\nRequirement already satisfied: numba>=0.48.0 in /opt/conda/lib/python3.7/site-packages (from pyts) (0.53.1)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from numba>=0.48.0->pyts) (49.6.0.post20210108)\nRequirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /opt/conda/lib/python3.7/site-packages (from numba>=0.48.0->pyts) (0.36.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn>=0.22.1->pyts) (2.1.0)\n\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n(50, 150) (50,)\n0.98\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/pyts/bag_of_words/bow.py:246: FutureWarning: BagOfWords has been reworked in 0.11 in order to match its definition in the literature. To get the old BagOfWords, use pyts.bag_of_words.WordExtractor instead.\n  \"instead.\", FutureWarning)\n","output_type":"stream"},{"name":"stdout","text":"0.9533333333333334\n","output_type":"stream"}]},{"cell_type":"code","source":"from pyts.utils import windowed_view\n\n# Load the data set and fit the classifier\nX, _, y, _ = load_gunpoint(return_X_y=True)\nclf = LearningShapelets(random_state=42, tol=0.01)\nclf.fit(X, y)\n\n# Select two shapelets\nshapelets = np.asarray([clf.shapelets_[0, -9], clf.shapelets_[0, -12]])\n\n# Derive the distances between the time series and the shapelets\nshapelet_size = shapelets.shape[1]\nX_window = windowed_view(X, window_size=shapelet_size, window_step=1)\nX_dist = np.mean(\n    (X_window[:, :, None] - shapelets[None, :]) ** 2, axis=3).min(axis=1)\n\nplt.figure(figsize=(14, 4))\n\n# Plot the two shapelets\nplt.subplot(1, 2, 1)\nplt.plot(shapelets[0])\nplt.plot(shapelets[1])\nplt.title('Two learned shapelets', fontsize=14)\n\n# Plot the distances\nplt.subplot(1, 2, 2)\nfor color, label in zip('br', (1, 2)):\n    plt.scatter(X_dist[y == label, 0], X_dist[y == label, 1],\n                c=color, label='Class {}'.format(label))\nplt.title('Distances between the time series and both shapelets',\n          fontsize=14)\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-09-03T08:02:05.028118Z","iopub.execute_input":"2021-09-03T08:02:05.028537Z","iopub.status.idle":"2021-09-03T08:02:27.855921Z","shell.execute_reply.started":"2021-09-03T08:02:05.028492Z","shell.execute_reply":"2021-09-03T08:02:27.854607Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass classes=[1 2], y=[2 2 1 1 2 2 2 2 2 1 1 1 1 1 2 1 2 2 1 2 1 1 1 2 1 2 1 1 2 1 1 2 2 1 2 1 2\n 2 2 2 2 1 1 1 2 2 1 2 1 2] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n  FutureWarning)\n/opt/conda/lib/python3.7/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n  return array(a, dtype, copy=False, order=order)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x288 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## [4. sktime](https://github.com/alan-turing-institute/sktime)\n\nhttps://github.com/alan-turing-institute/sktime/blob/master/examples/02_classification_univariate.ipynb\n\nhttps://github.com/sktime/sktime-dl/blob/master/examples/univariate_time_series_classification.ipynb","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}